Progress 09/01/23 to 08/31/24
Outputs
Target Audience:AIIRA reaches a broad and diverse audience of USDA/NIFA stakeholders through our research and outreach activities. • K-12 students (middle and high school) including students of color and students from economically disadvantaged backgrounds • Graduate students and postdoctoral fellows • Undergraduate and community college students including women, native american and hispanic populations • Farmers and commodity groups including Indigenous Farmers • Extension and teaching professionals, Ag professionals • USDA ARS scientists • Industry Changes/Problems:AIIRA had several faculty moves over the past year. Carolyn Lawrence-Dill left Iowa State University for a new role at Colorado State University and is no longer with AIIRA. Lizhi Wang and Michelle Segovia moved universities but continue to contribute to AIIRA through their new institutions (adding two new subawards). We also said goodbye to three other Sr. Personnel due to a shift in research to our moonshots. Sotirious Archontoulis's contributions in years 1-3 were valuable for providing data needed to move towards our moonshot challenges. Shawn Dorius's farmer surveys were also highly valuable, but he no longer has support from his college to continue with the surveys. Cassandra Dorius accepted a position with the US census and is no longer with AIIRA. We also face the challenge of moving forward with our AIIRA goals without continued funding beyond year 5. What opportunities for training and professional development has the project provided?AIIRA's education and workforce development opportunities aim to develop talent and skills for a highly competent next generation AI workforce which includes activities for graduate and undergraduate students, and continual learners of all ages. A diverse team from across AIIRA's collaborative institutions aim to leverage existing partnerships to create new learning opportunities and expand existing programs aimed at advancing knowledge and education in AI in Ag. University and college: AIIRA personnel directly mentored 4 postdocs, 67 graduate students, and 18 undergraduates across 12 institutions. Mentoring consisted of both formal course work, training and research assistantship, as well as informal networking across institutions. We briefly detail selected activities below, with additional details in the full report to the cognizant program manager. Undergrad/grad education: 1) Hosted 8 undergraduates for a 9-week undergraduate research experience. 2) Began collaborations with Col Reed Simmons at CMU on an advanced AI course Autonomous Agents where students work to develop an AI agent that can control a self-contained greenhouse autonomously. 3) Contributed to Iowa State University's first Master's in Artificial Intelligence as well as an undergraduate minor in Applied Artificial Intelligence that is applicable to all ISU majors. The program includes an Introduction to Applied AI course and research workshops and programs that demonstrate how AI can be applied to different domains. In collaboration with TrAC, provided several opportunities for graduate students to develop and enhance their AI skills including 1) a week long "Deep Dive into AI" bootcamp (also held for REU students), 2) Two 1-day long tutorials on Demystifying Trending AI Techniques and Prompt Engineering and RAG, 3) 18 Seminars presented to a broad audience of students, faculty and interested industry professionals, 4) 17 mini-courses on applications of AI designed for students to explore applying AI to their discipline. Workforce development via Ag-AI related training opportunities: AIIRA hosted the first AI-in-Ag Hackathon brought together undergraduates, graduate students, and immigrant farmers to generate solutions to ag problems identified by farmers. Workforce development via connecting with stakeholders: AIIRA completed activities to generate a new AI-in-Ag workforce. Efforts were focused on identifying means to connect with farmers and growers to demonstrate AI-driven tools and technologies and to showcase AI-driven technologies to the broader agricultural community. 1) AIIRA researchers demonstrated advances in AI Tools to the Iowa Soybean Association board of directors and gathered feedback to help inform tool development, 2) AIIRA presence at the 2024 Farm Progress Show in Boone, IA, with over 10,000 visitors to the tent. Workforce development via community building activities: We organized several workshops to disseminate research and education advances to various stakeholders including: 1) AIIRA demonstrations for the Iowa Soybean Association Board on June 18, 2024. 2) Demonstrations of the Weed app at the 2024 Farm Progress Show in Boone, IA. 3) International Workshop on Machine Learning for Cyber Ag systems (MLCAS) planned for October 7, 2024. Pre-college education: AIIRA's pre-college education efforts focused on leveraging existing partnerships at CMU with a goal to develop a new curriculum in Pittsburgh, PA through the Urban Robotony (an AIIRA Signature Activity) workshop for middle schoolers. As a signature AIIRA effort, the program is a hands-on program targeting middle school students that combines AI, robotics, and plant science. 1) AIIRA's CMU team partnered with the President's office at Carnegie Mellon University to bring the Center of Life Camp in Hazelwood to CMU's campus four Thursdays this summer. We provided STEM and Robots in Agriculture activities each week. AIIRA has built a robot to take photos of plants that the students can see and drive. Students had hands-on experience with manipulating a robot and learning how robots are created. 2) ISU's summer internships invited several high school students to participate in research activities in AIIRA. The 6 students participated in activities including 3Dmodeling, learning visualization tools, and creating evaluation models. One such research project (SDF for Maize PCD) provided the opportunity for 2 high school students to train machine learning models to reconstruct a 3D shape given a set of 3D points from the shape. 3) The Women in Agriculture and AI program also provided several activities to engage k-12 students in STEM including connecting with 4H Youth Leaders through an Ag Innovators Experience at ISU and demonstrating a rover activity at the Iowa State University Science Fair. How have the results been disseminated to communities of interest?AIIRA results are disseminated through collaboration and knowledge transfer workshops and training, sharing of benchmark datasets, and participation in field days to engage directly with stakeholders. Multidisciplinary Integration and Community Building. AIIRA has worked towards creating a learning community to support a collaborative research and training environment across the transdisciplinary boundaries of team members. We are administratively housed within the Center for Translational AI (TrAC) at ISU specifically to facilitate collaboration and knowledge transfer activities. With the goal of providing the means to support collaboration and sharing of knowledge, and using team science-based approaches, many workshops and training have been established or shared across the team and AI-in-Ag community. AIIRA sponsored workshops and training: AIIRA sponsored, developed, and implemented several workshops and training opportunities to AIIRA stakeholders. 1) 5 Seminar Series presentations on applying AI, 2) "Deep Dive into AI" a 4-day comprehensive bootcamp into the world of artificial intelligence including practical exercised and workshops, attended by undergraduates, graduate students and industry professionals, and 3) "Demystifying Trending AI Techniques" tutorial. Intra-AIIRA Collaboration: We continue efforts to connect AIIRA faculty, researchers and graduate students across disciplines. For a third year, CMU team members visited Ames, IA to test their robotics in real world scenarios with ISU team members on site to provide context and valuable feedback. CMU made three visits to Iowa in the summer and fall of 2024 in 1) June 24-28 to field test the corn sensor insertion arm on the amiga robot, 2) July 22-25 for robot testing, and 3) in mid-September to test the newly remodeled dexterous robot hand. Additionally, as a result of the Arboretum project, AIIRA faculty and students have begun activities to develop best practices for implementing such projects with the intent of ensuring future projects run as smoothly as possible, and sharing the best practices with the AIVO organizations. Field Days: Engaging AIIRA's scientists, engineers and social scientists through well established, outward facing, field days and training workshops have been key to identifying the needs of our stakeholders and toward disseminating AI advances and impacts to agriculture. 1) AIIRA students demonstrated AI capabilities to the ISA board of directors and gathered impactful feedback for future development, 2) the updated InsectNet app was presented to over 10,000 farmers and decision makers at the 2024 Farm Progress show in Boone, IA, 3) over 3000 ISU undergraduates learned more about AIIRA and the future of AI in Agriculture at John Deere Days at Iowa State University's central campus in late August. Multi-organizational Synergies / Achievements. AIIRA strives to create community amongst our team members and partner institutions. 1) AIIRA, in collaboration with our sister AI-for-Ag USDA funded AI Institutes, organized and convened the first AI for Agriculture Summit in Washington DC July 29-30, 2024. The goal of the Summit was to bring together thought leaders and policy makers to discuss and envision the future of agriculture through AI. 2) We hosted Carrie Alexander, from AI Institute for Next Generation Food Systems (AIFS) in our TrAC Seminar Series with a record number of attendees, and the session went over time due to the Q&A and interest in the ethical framework shared. 3) AIIRA has developed an integral collaboration with ICICLE to better train AIIRA models using ICICLE techniques. 4) AIIRA is partnering with AI-SDM on broadening participation and education and workforce development efforts including working with ISA on identifying and addressing barriers to adoption, leveraging AI-SDM relationships in Africa and India to deploy AIIRA tools to a broader, international audience, and utilize AI-SDM's expertise in elicitation to build models and fine tune LLMs with reinforcement learning and feedback. Knowledge Transfer. Our knowledge transfer and dissemination of results to research communities and communities of practice has been productive in our third year. While there have been no patents submitted or technology transfer, we have been productive in generating publications, presentations, events, activities, and other products Broadening Participation: AIIRA aims to apply best practices for mentoring and engaging women, Hispanics, and Native Americans in AI using evidence-based strategies across these underrepresented populations through dedicated signature activities of AI-In Ag Training for Native American Students, Native American farmers bi-directional engagement, and Women in Agriculture and AI (WIAA). 1) AIIRA, in collaboration with the Native Nations Institute at University of Arizona, focused on two concrete efforts engaging with Native Americans. The AI/Ag workshop explored the landscape of Indigenous agriculture data, current agriculture data needs of Indigenous Peoples in the US, and better collection of Indigenous agriculture data. Informed by a series of facilitated discussions and participant dialogue, the Collaboratory team developed a set of recommendations to improve Indigenous agriculture data through the application of Indigenous Data Sovereignty. Through Bi-Directional engagement we are working to build the modules onto the Indigenous DataSET Resource Hub. 2) WIAA students have engaged with 4-H coordinators and Women in Science and Engineering at ISU, as well as embedded themselves within PhenoRob, a research institute in Bonn Germany to share how WIAA is generating impact here at ISU. 3) A new project was undertaken to investigate how large language models can address instructional gaps for blind and visually impaired students interested in agricultural careers through the use of tactile 3D printed graphics. 4) AIIRA has the ability to now directly expand our AIIRA outreach through participation in the Society for Advancement of Chicanos/Hispanics & Native Americans in Science (SACNAS) and the American Indian Science and Engineering Society (AISES). 5) AIIRA writing contest asked high school students to envision their future careers as they will be impacted by AI. AIIRA has also disseminated results through 51 publications, 31 presentations, 7 activities, 38 events, and 8 other products. What do you plan to do during the next reporting period to accomplish the goals?Research activities: We will continue to expand and make progress on our research goals and activities initiated in years 1-3 through combining results and efforts into our moonshot projects. New projects will be identified in our all-team meeting in early September 2024. Education and workforce development and broadening participation plans: 1) expand our native american outreach through collaboration with AI-DSM, and new memberships in SACNAS and AESIS, 2) Continue efforts (in part in collaboration with AI-SDM) to gather farmer/grower feedback on AIIRA tools, 3) Collaborate with ISU partners on an AI Makerspace in the Student Innovation Center, 4) grow relationships with HBCUs and MSIs through Expand AI efforts.
Impacts
What was accomplished under these goals?
Overall Impact Statement:? Our research activities focused on making progress towards four distinct moonshots to achieve our goals. Goal 1: Build plant and field scale predictive models through foundational AI advances. AI Agents for Pest Identification and Mitigation focuses on the development and application of AI-driven agents designed to identify, classify and mitigatepests and diseases in agriculture. Our research integrates advanced machine learning techniques, sensor technologies, and domain-specific knowledge to address challenges in pest management. AI-driven pest identification systems leverage sophisticated machine learning models to accurately detect and classify pests and diseases that threaten crops. We aim to make the models more adaptable to different environments and crop types. Sensor-based pest detection is another critical component. Advanced sensors enable the rapid and precise detection of pathogens at an early stage, which is crucial for effective pest management. These sensors can be integrated with AI agents to provide real-time monitoring and alert systems, allowing for timely interventions that can prevent widespread crop damage. The research on zero-shot and few-shot learning introduces methods that allow AI systems to recognize new pest species or diseases with minimal training data. This is valuable in dynamic agricultural environments where new threats can emerge suddenly. By leveraging weak language supervision and multimodal data, these systems can generalize well to unseen scenarios, making them robust and versatile tools in pest management. Collectively, this research contributes to the development of systems that identify and classify pests and suggest or implement mitigation strategies, thereby enhancing the resilience and sustainability of agricultural practices. Multi-Agent Adaptive Sensing encompasses the deployment of multiple agents, such as robots and sensor networks, that collaborate to perform adaptive sensing tasks in agriculture. Multi-agent adaptive sensing is a challenging goal approach in precision agriculture, where multiple robotic or sensor agents are deployed to monitor and interact with the environment autonomously. These agents will allow them to make real-time decisions about where, when, and how to collect data, thereby optimizing the sensing process to ensure comprehensive coverage and high-quality data collection. A core component is the development of algorithms for multi-robot adaptive sampling and informative path planning. These algorithms allow robots to coordinate their movements and data collection strategies based on real-time environmental data, enabling efficient monitoring of large agricultural areas. These systems are designed to navigate through obstacles and position sensors optimally, ensuring that data is collected from all necessary locations. Another important aspect is the integration of predictive models with adaptive sensing systems. By using reinforcement learning-based approaches, these systems can predict environmental dynamics and adjust their data collection strategies accordingly. This is particularly useful in situations where the environment is constantly changing, such as in response to weather patterns or crop growth stages. Additionally, activities in this moonshot explore the concept of next-best-view planning, where sensors or robots are guided to positions that maximize the information gained from each observation. This approach is critical for applications such as fruit sizing or disease detection, where the ability to view an object from multiple angles can significantly improve measurement accuracy. Goal 2: Deploy plant and field scale predictive models for breeding and crop production applications. Multi-Modal Foundational Models focus on collecting multimodal data and creating and refining foundational AI models that integrate various data modalities to support a wide range of agricultural applications. We explored the development of large-scale, multimodal datasets and the application of these datasets to train AI models capable of performing complex tasks in agriculture. The development of multimodal foundational models is a transformative approach in AI for agriculture, addressing the need for comprehensive and versatile systems that can process and analyze diverse data types. These models are designed to handle data from multiple sources, such as hyperspectral imaging, 3D point clouds, and textual descriptions, and to integrate this information to make informed decisions. One significant contribution is the creation of large-scale datasets like the "Arboretum," which provides a rich collection of biodiversity-related data across multiple modalities. The integration of multimodal data into AI models allows for the extraction of richer, more nuanced information than single-modality models. The research on class-specific data augmentation further enhances these models by generating synthetic data that improves the models' robustness to variations in the input data. Moreover, the use of zero-shot and few-shot learning techniques within these models is particularly innovative, enabling the models to adapt to new tasks or datasets with minimal additional training. This is essential for agricultural applications where new data types or conditions frequently arise, such as novel plant diseases or changing environmental conditions. Goal 3: Understanding and resolving social barriers to, and AI innovations for adoption of the AI technology in the agricultural ecosystem Data Co-op focuses on the creation, sharing, and governance of agricultural data through cooperative frameworks. The research addresses issues related to data standardization, accessibility, data sovereignty, and collaborative research efforts, aiming to foster broader AI adoption and innovation in agriculture. The concept of a Data Co-op in agriculture is centered around the idea of collaborative data sharing and governance, where stakeholders contribute to and benefit from a shared pool of agricultural data. This moonshot emphasizes the importance of data standardization, open-access platforms, and the ethical use of data to drive innovation in AI-driven agricultural practices. One contribution was the development of frameworks that combine translational research with behavioral economics to understand and promote the adoption of AI technologies in agriculture. This research highlights the challenges and opportunities in creating data-driven solutions that are accessible and beneficial to a wide range of users, from smallholder farmers to large agricultural enterprises. Data governance and sovereignty are also major themes. Research on Indigenous data sovereignty, for example, explores how tribal agricultural data needs can be met while respecting the rights and traditions of Indigenous communities. This work underscores the importance of ethical considerations in data sharing and the need for governance models that empower all stakeholders, particularly marginalized groups. The integration of AI with data-sharing platforms facilitates the annotation and standardization of diverse agricultural datasets. This not only improves the usability and interoperability of data but also supports the creation of more robust and generalizable AI models. Finally, we put in significant effort in the creation of common datasets. By making such data widely available through cooperative frameworks, researchers and breeders can collaborate more effectively, accelerating the pace of agricultural innovation. Goal 4: Create a diverse, AI-aware agricultural workforce and serve as a nexus for AI-in-Ag developments: Described in detail in next section.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Lee, M., Berger, A., Guri, D., Zhang, K., Coffey, L., Kantor, G., & Kroemer, O. (2024). Towards Autonomous Crop Monitoring: Inserting Sensors in Cluttered Environments. IEEE Robotics and Automation Letters.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Kailas, S., Luo, W., Sycara, K. (2023). Multi-robot Adaptive Sampling for Supervised Spatiotemporal Forecasting. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebasti�o, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14115. Springer, Cham. https://doi.org/10.1007/978-3-031-49008-8_28
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
AMA Style
Young TJ, Chiranjeevi S, Elango D, Sarkar S, Singh AK, Singh A, Ganapathysubramanian B, Jubery TZ. Soybean Canopy Stress Classification Using 3D Point Cloud Data. Agronomy. 2024; 14(6):1181. https://doi.org/10.3390/agronomy14061181
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Guo X, Qiu Y, Nettleton D, Schnable PS. High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants. Plant Phenomics 2023;5:Article 0052. https://doi.org/10.34133/plantphenomics.0052
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Kim, C. H., Lee, M., Kroemer, O., & Kantor, G. (2024, May). Towards robotic tree manipulation: Leveraging graph representations. In 2024 IEEE International Conference on Robotics and Automation (ICRA) (pp. 11884-11890). IEEE.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Freeman, H., & Kantor, G. (2024, May). Autonomous Apple Fruitlet Sizing with Next Best View Planning. In 2024 IEEE International Conference on Robotics and Automation (ICRA) (pp. 15847-15853). IEEE.
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Luxenberg, E., Malik, D., Li, Y., Singh, A., & Boyd, S. (2024). Specifying and Solving Robust Empirical Risk Minimization Problems Using CVXPY. Journal of Optimization Theory and Applications, 1-11.
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
�Hingle, A. & Johri, A. (2024). A Framework to Develop and Implement Role-Play Case Studies to Teach Responsible Technology Use. IEEE Transactions on Technology and Society. doi: 10.1109/TTS.2024.3408085
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Ranade, N., Saravia, M. & Johri, A. (2024). Using rhetorical strategies to design prompts: a human-in-the-loop approach to make AI useful. AI & Society. https://doi.org/10.1007/s00146-024-01905-3
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Almatrafi, O., Johri, A. & Lee, H. (2024). A Systematic Review of AI Literacy Conceptualization, Constructs, and Implementation and Assessment Efforts (2019-2023). Computers & Education Open.� https://doi.org/10.1016/j.caeo.2024.100173?
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Schleiss, J. & Johri, A. (2024). A Roles-based Competency Framework for Integrating Artificial Intelligence (AI) in Engineering Courses. Proceedings of SEFI 2024.�
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Schleiss, J., Johri, A. & Strober, S. (2024). Integrating AI Education in Disciplinary Engineering Fields: Towards a Systems and Change Perspective. Proceedings of SEFI 2024
- Type:
Journal Articles
Status:
Submitted
Year Published:
2024
Citation:
Tross MC, Duggan G, Shrestha N, Schnable JC. Models trained to predict differential expression across plant organs identify distal and proximal regulatory regions. Submission Planned 5/17/2024
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Johri, A. & Hingle, A. (2024). Using Deliberate Discussions to Develop a Responsible Engineering Mindset Among Students. Proceedings of SEFI 2024.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Johri, A., Collier, A., Jesiek, B., Korte, R. & Brozina, C. (2024). Workplace Learning Ecology of Software Engineers and Implications for Teaching and Learning. Proceedings of IEEE CSEE&T 2024.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Johri, A. & Hingle, A. (2024). Case Study Based Pedagogical Intervention for Teaching Software Engineering Ethics. Proceedings of IEEE CSEE&T 2024.�
- Type:
Other
Status:
Published
Year Published:
2024
Citation:
Hingle, A. & Johri, A. (2024). Role-Play Case Studies to Teach Computing Ethics: Theoretical Foundations and Practical Guidelines. Proceedings of HICSS 2024. Best Paper Award Nominee.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Hingle, A., Katz, A. & Johri, A. (2023). Exploring NLP-based Methods for Generating Engineering Ethics Assessment Qualitative Codebooks. Proceedings of FIE 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Mehta, S., *Hingle, A. & Johri, A. (2023). Identifying E-Scooter Ethical Dilemmas through Case Studies. Proceedings of FIE 2023.�
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Johri, A., Lindsay, E. & Qadir, J. (2023). Ethical Concerns and Responsible Use of Generative Artificial Intelligence in Engineering Education. Proceedings of SEFI 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Mehta, S., Hingle, A. & Johri, A. (2023). Developing Perspectival Thinking Related to Sustainability through Case Study Discussions. Proceedings of SEFI 2023.�
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Hingle, A. & Johri, A. (2023). Recognizing Principles of AI Ethics through a Role-Play Case Study on Agriculture. Proceedings of ASEE 2023.�
- Type:
Journal Articles
Status:
Awaiting Publication
Year Published:
2024
Citation:
Shrestha N, Powadi A, Davis J, Ayanlade TT, Liu H, Tross MC, Mathivanan RK, Bares J, Lopez-Corona L, Turkus J, Coffey L, Jubery TZ, Ge Y, Sakar S, Schnable JC, Ganapathysubramanian B, Schnable PS. Crop performance, aerial and satellite data From multistate maize yield trials. bioRxiv (doi pending as of 5/9/2024)
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Sahay S, Shrestha N, Moura Dias H, Mural RV, Grzybowski M, Schnable JC, Glowacka K Comparative GWAS identifies a role for Mendels green pea gene in the nonphotochemical quenching kinetics of sorghum, maize, and arabidopsis. bioRxiv doi: 10.1101/2023.08.29.555201
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Torres-Rodriguez JV, Li D, Turkus J, Newton L, Davis J, Lopez-Corona L, Ali W, Sun G, Mural RV, Grzybowski M, Thompson AM, Schnable JC (2024) Population level gene expression can repeatedly link genes to functions in maize. The Plant Journal. bioRxiv doi: 10.1101/2023.10.31.565032
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Sahay S, Grzybowski M, Schnable JC, Glowacka K (2023) Genetic control of photoprotection and photosystem II operating efficiency in plants. New Phytologist doi: 10.1111/nph.18980
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Tross MC, Grzybowski M, Jubery TZ, Grove RJ, Nishimwe AV, Torres-Rodriguez JV, Sun G, Ganapathysubramanian B, Ge Y, Schnable JC (2024) Data driven discovery and quantification of hyperspectral leaf reflectance phenotypes across a maize diversity panel. The Plant Phenome Journal. bioRxiv doi:10.1101/2023.12.15.571950
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Jin H, Tross MC, Tan R, Newton L, Mural RV, Yang J, Thompson AM, Schnable JC (2024) Imitating the breeders eye : predicting grain yield from measurements of non-yield traits. The Plant Phenome Journal doi: 10.1002/ppj2.20102 bioRxiv doi: 10.1101/2023.11.29.568906
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Mark Sheinin, Aswin C. Sankaranarayanan, and Srinivasa G. Narasimhan. "Projecting Trackable Thermal Patterns for Dynamic Computer Vision." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 25223-25232. 2024.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Grzybowski M, Mural RV, Xu G, Turkus, J, Yang Jinliang, Schnable JC (2023) A common resequencing-based genetic marker dataset for global maize diversity. The Plant Journal doi: 10.1111/tpj.16123 Cover Article, March 2023 Research Highlight in The Plant Journal doi: 10.1111/tpj.16123
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Yin, S., & Dong, L. (2024). Plant Tattoo Sensor Array for Leaf Relative Water Content, Surface Temperature, and Bioelectric Potential Monitoring. Advanced Materials Technologies, 2302073.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Singh N, Khan RR, Xu W, Whitham SA, Dong L. Plant virus sensor for the rapid detection of bean pod Mottle virus using virus-specific nanocavities. ACS sensors. 2023 Sep 22;8(10):3902-13.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Dominik Bauer, Cornelia Bauer, and Nancy S. Pollard, Design from Demonstrations:� Creating Complex Printable Soft Robot Hands from Human Demonstrations, Can We Build Baymax? Workshop, IEEE-RAS International Conference on Humanoid Robots (Humanoids), Austin, TX, December, 2023.��
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Mannam, Pragna, Xingyu Liu, Ding Zhao, Jean Oh, and Nancy Pollard. "Design and Control Co-Optimization for Automated Design Iteration of Dexterous Anthropomorphic Soft Robotic Hands." In 2024 IEEE 7th International Conference on Soft Robotics (RoboSoft), pp. 332-339. IEEE, 2024.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Mannam, Pragna, Kenneth Shaw, Dominik Bauer, Jean Oh, Deepak Pathak, and Nancy Pollard. "Designing anthropomorphic soft hands through interaction." In 2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids), pp. 1-8. IEEE, 2023
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2024
Citation:
Mani Ramanagopal, Sriram Narayanan, Aswin C. Sankaranarayanan, and Srinivasa G. Narasimhan. "A Theory of Joint Light and Heat Transport for Lambertian Scenes." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11924-11933. 2024.
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2024
Citation:
Sriram Narayanan, Mani Ramanagopal, Mark Sheinin, Aswin C. Sankaranarayanan, and Srinivasa G. Narasimhan. Shape from Heat Conduction. accepted to European Conference on Computer Vision (ECCV) 2024.
- Type:
Other
Status:
Published
Year Published:
2024
Citation:
Jennings, L., Martinez, A., Cummins, J., Lameman, B., Wiipongwii, T., Arcand, M., Moore, K., Lee, J., Nuvamsa, M., Plenty Grass-She Kills, R., Gazing Wolf, J., Carroll, S. 2024. Intersections of Indigenous Data Sovereignty and Tribal Agricultural Data Needs in the US. Tucson, AZ. Collaboratory for Indigenous Data Governance. DOI: 10.6084/m9.figshare.26156170
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Feuer, B., Pham, M., Joshi, A., & Hegde, C. (2023). Distributionally robust classification on a data budget. Transactions on Machine Learning (TMLR), August 2023.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
McElfresh, D., Khandagale, S., Valverde, J., Feuer, B., Hegde, C., Ramakrishnan, G., Goldblum, M., & White, C. (2023). When do neural networks outperform boosted trees on tabular data? Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks, December 2023.
- Type:
Other
Status:
Published
Year Published:
2023
Citation:
Feuer, B., Cohen, N., & Hegde, C. (2023). Scaling TabPFN: Sketching and feature selection for prior-fitted networks. NeurIPS TRL Workshop, December 2023.
- Type:
Other
Status:
Published
Year Published:
2023
Citation:
Feuer, B., & Hegde, C. (2023). Exploring dataset-scale indicators of data quality. NeurIPS Attrib Workshop, December 2023.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Feuer, B., Joshi, A., Cho, M., Chiranjeevi, S., Deng, Z., Balu, A., Singh, A., Sarkar, S., Merchant, N., Ganapathysubramanian, B., & Hegde, C. (2024). Zero-shot insect detection via weak language supervision. The Plant Phenome Journal, May 2024.
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Feuer, B., Liu, Y., Freire, J., & Hegde, C. (2024). ArcheType: Open-source column type annotation using large language models. Very Large Databases (VLDB) Journal, August 2024.
- Type:
Other
Status:
Published
Year Published:
2024
Citation:
H.-Yang, C., Feuer, B., Jubery, Z., Deng, Z., Nakkab, A., Hasan, M., Chiranjeevi, S., Marshall, K., Baishnab, N., Singh, A. K., Singh, A., Sarkar, S., Merchant, N., Hegde, C., & Ganapathysubramanian, B. (2024). Arboretum: A large multimodal dataset enabling AI for biodiversity. In submission, June 2024.
- Type:
Journal Articles
Status:
Published
Year Published:
2024
Citation:
Yanarella, C. F., Fattel, L., & Lawrence-Dill, C. J. (2024). Genome-wide association studies from spoken phenotypic descriptions: A proof of concept from maize field studies. G3 Genes|Genomes|Genetics.
|
Progress 09/01/22 to 08/31/23
Outputs
Target Audience:AIIRA reaches a broad and diverse audience of USDA/NIFA stakeholders through our research and outreach activities. • K-12 students (middle and high school) including students of color and students from economically disadvantaged backgrounds • Graduate students and postdoctoral fellows • Undergraduate and community college students including women, native american and hispanic populations • Farmers and commodity groups including Indigenous Farmers • Extension and teaching professionals, Ag professionals • USDA ARS scientists • Industry Changes/Problems:Other personnel: Aaron Presholt was serving as an interim PI from Iowa Soybean Association. He was replaced by Matthew Carroll in Spring 2023. Dr. Carroll is very familiar with many aspects of the AIIRA project as former graduate student within AIIRA. Budget: AIIRA's year 2 funding was not received until December 2022 even thought the project year began in September. As we were underspent in year 1, we were able to obtain a no cost extension to cover student stipends and tuition until the year 2 funds arrived, but it was an unexpected challenge to maintain continuous support during the delay. What opportunities for training and professional development has the project provided?AIIRA's education and workforce development opportunities aim to develop talent and skills for a highly competent next generation AI workforce which includes activities for graduate and undergraduate students, and continual learners of all ages. A diverse team from across AIIRA's collaborative institutions aim to leverage existing partnerships to create new learning opportunities and expand existing programs aimed at advancing knowledge and education in AI in Ag. University and college: AIIRA personnel directly mentored 7 postdocs, 84 graduate students, and 12 undergraduates across 8 institutions. Mentoring consisted of both formal course work, training and research assistantship, as well as informal networking across institutions. We briefly detail selected activities below, with additional details in the full report to the cognizant program manager. Undergrad/grad education: 1) We made progress planning for undergraduate programming in AI-in-Ag (an AIIRA Signature Activity). AI for Robotics course was developed and taught at CMU. 2) GMU has prepared a case study course on ethics of use in AI for Ag. 3) ISU has developed an undergraduate minor in engineering called Cyber-Physical Systems (an AIIRA Signature Activity) with modules planned for AI-in-Ag with a capstone course that serves as a template AIIRA can use for a graduate minor. 4) We are planning for Ag-AI content in the recently offered Data Science minor at ISU, focused on undergrads from Ag and Life Sciences. 5) ISU has launched an entrepreneurial marketing course wherein students are working on marketing projects for Cyverse and Big Data in a Box. Founders of these entities have given guest talks to students. In addition, non-profit organizations in the ag and food security space such as Cultivating Hope Farms (a farm for persons with disabilities run by a woman farmer) and E-Feed Hungers (a social enterprise linking food banks with beneficiaries) are also collaborating with the entrepreneurial marketing students. 7) Chinmay Hedge served as a GSTEM (formerly Girls in Stem) mentor at NYU Tandon in the summer of 2023, 8) ISU's AGRON 279 Field Exploration in Agronomy demonstrated to undergraduate students on what a self-pollinated species breeding program looks like with a focus on technology for farmers like the insect app, and 9) ISU's AGRON 183 highlighted the applications of UAVs and Rovers in agricultural studies. Workforce development via Ag-AI related training opportunities: 1) We offered application development exploring various AI and software tools with several honors freshmen, one of which built a prototype for their summer research. 2) Provided opportunities for AI, digital transformation and digital agriculture research involving one honors marketing undergraduate Grace Pearson and an MBA graduate student Amrutha Kandhare (both women), 3) We mentored students on entrepreneurial marketing projects in digital agriculture, agricultural and applied economics as it relates to ethical and social barriers of AI-in-Ag as well as in Computer Science and Engineering introducing them to digital agriculture research. 4) Four graduate and two undergraduate students worked on different aspects of AI, AI Ethics, and AI ethics and agriculture related projects resulting in case studies for instruction and implemented in an undergraduate course in Fall 2022. 5) The development of ML algorithms by two undergraduate students, who took into account interaction with humans and the capabilities of human feedback. 6) Exposed a sustainable agriculture & sociology PhD student to the social dimensions of AI-in-Ag literature and research and provided opportunities to present their work at research group meetings or to the research community. 7) Several engineering undergraduates were exposed to AI-in-Ag research opportunities. 8) Generated engineering capstone design teams. 9) We provided professional presentation coaching to five AIIRA graduate students (3 women) in preparation for their presentations to our USDA cognizant program leader at our annual review. 10) Currently working with an under-graduate research assistant Yufei Zhang and a graduate assistant Prathiksha Ravi Krishnan (both women) on consumer research on AI, sustainability and ag. Workforce development via connecting with stakeholders: AIIRA completed activities to generate a new AI-in-Ag workforce. 1) Efforts were focused on identifying means to connect with farmers and growers to demonstrate AI driven tools and technologies as well as means to showcase AI driven technologies to the broader agricultural community. 2) AIIRA presence at the 2022 Farm Progress Show in Boone, IA, with over 10,000 visitors to the tent. 3) The Data Science for the Public Good, Wholesale Local Food Benchmarking Project sponsored by AIIRA Expanded on the previous year's project. The Data-drive Insights for Local Food Markets: AI, Pricing, and Crop Flow project brought together one undergraduate student, three graduate students (with degrees in Management Information Systems, Data Science, History, Industrial Engineering and AI), extension professionals, and several support faculty to enhance local food markets by providing valuable insights and optimizing the crop flow. Results benefit farmers by creating the ability to provide personalized recommendations regarding which crops to grow based on their respective locations. Workforce development via community building activities: We organized several workshops to disseminate research and education advances to various stakeholders including: 1) International Workshop on Machine Learning for Cyber Ag systems (MLCAS) held July 3-5 in Japan, 2) Translational AI Industry and Academia Symposium in June 2023 connected Industry representatives from across Iowa to AIIRA AI experts to discuss ways in which Iowa State University and Industry can expedite adoption of Artificial Intelligence, Machine Learning, and Big Data Analytics. AIIRA met with over 20 organizations interested in expanding workforce development across Iowa. Pre-college education: AIIRA's pre-college education efforts focused on leveraging existing partnerships at CMU with a goal to develop a new curriculum in Pittsburgh, PA through the Urban Robotony (an AIIRA Signature Activity) workshop for middle schoolers. As a signature AIIRA effort, the program is a hands-on program targeting middle school students that combines AI, robotics, and plant science. The second year of the program continued on the success of the first with the second iteration of Urban Robotony occurring in July 2023. 2) ISU's summer internships resulted in three high school students learning machine learning coding as it relates to field corn and common agricultural insects. AIIRA sponsored a unique informal learning opportunity to engage with undergraduate students outside of the AI and Agriculture frames. A. Johri (GMU) and an AIIRA graduate student implemented the AIIRA Writing Contest asking applications to either write a letter imaging what a meal could be in 2050 and the technology used to produce, transport, and prepare that meal, or create a menu or recipe that describes the sourcing and production of ingredients. Both scenarios encourage the writer to consider ethical dilemmas involved. The contest received 160 entries and awarded 3 overall prize winners, 3 separate winners to Youngstown University students and 4 honorable mentions. The contest will continue in Fall 2023 and be expanded to include high school entrants. How have the results been disseminated to communities of interest?AIIRA results are disseminated through collaboration and knowledge transfer workshops and training, sharing of benchmark datasets, and participation in field days to engage directly with stakeholders. Multidisciplinary Integration and Community Building. AIIRA has worked towards creating a learning community to support a collaborative research and training environment across the transdisciplinary boundaries of team members. We are administratively housed within the Center for Translational AI (TrAC) at ISU specifically to facilitate collaboration and knowledge transfer activities. With the goal of providing the means to support collaboration and sharing of knowledge, and using team science-based approaches, many workshops and training have been established or shared across the team and AI-in-Ag community. AIIRA sponsored workshops and training: AIIRA sponsored, developed, and implemented several workshops and training opportunities to AIIRA stakeholders. 1) TrAC Journal Club included AIIRA graduate students who participated in 10 sessions offered in Fall 2022 through Spring 2023. 2) TrAC Seminar Series sponsored by AIIRA presented to a wide audience across disciplines. 3) ML for Cyber-Agricultural Systems (October 2022). 4) Fundamentals of Deep Learning (November 2022) 5) Trending AI Techniques (April 2023) 6) Fifth International Workshop of Machine Learning for Cyber-Agricultural Systems (MLCAS 2023) workshop (July 2023) aimed to bring together academic and industrial researchers and practitioners in the fields of machine learning, data science and engineering, plant sciences and agriculture, to identify and discuss major technical challenges and recent results related to machine learning-based approaches. 7) AI for Agriculture and Food Systems, in conjunction with AAAI Conference and in collaboration with AgAID, AIFARMS, and AIFS. Intra-AIIRA Collaboration: We continue efforts to connect AIIRA faculty, researchers and graduate students across disciplines. For a second year, CMU team members visited Ames, IA to test their robotics in real world scenarios with ISU team members on site to provide context and valuable feedback. CMU made three visits to Iowa in the summer and fall of 2023 in 1) June 19-23 to field test the corn sensor insertion arm on the amiga robot, 2) July 24 - August 1 to field testing in soybeans, and 3) in mid-September to test the newly remodeled dexterous robot hand. Field Days: Engaging AIIRA's scientists, engineers and social scientists through well established, outward facing, field days and training workshops have been key to identifying the needs of our stakeholders and toward disseminating AI advances and impacts to agriculture. 1) The Workshop on Survey design and Experimental Methods in Applied and Agricultural Economics resulted in identifying recommendations for stakeholder engagement and policy design, 2) field demonstrations at the National Association of Plant Breeders Field Tour resulted in sharing research on sensors, root imaging and abiotic stress breeding, 3) Iowa Soybean Association field demonstrations showcasing research progress, and 4) collaborations with the HIPS and AG2PI offer opportunities for AIIRA to participate in planned field days Spring and Summer 2023. Social Media: With 356 Twitter followers, 137 LinkedIn followers and 56 Instagram followers, our outreach has grown over the past year. Posts shared include unique AIIRA content and sharing of AIIRA and AIIRA Institution related themes. Multi-organizational Synergies / Achievements. AIIRA strives to create community amongst our team members and partner institutions. AIIRA activity participates in AIVO efforts connecting with other AI Institutes on multiple fronts including sharing lessons learned with new AI Institutes. Knowledge Transfer. Our knowledge transfer and dissemination of results to research communities and communities of practice has been productive in our first year. While there have been no patents submitted or technology transfer, we have been productive in generating publications, presentations, events, activities, and other products Broadening Participation: AIIRA aims to apply best practices for mentoring and engaging women, Hispanics, and Native Americans in AI using evidence-based strategies across these underrepresented populations. (1) AIIRA, in collaboration with the Native Nations Institute at University of Arizona developed a Software Carpentry based learning material for ML/AI instruction with an Indigenous data focus. Progress on the Carpentries course development continues to move forward creating trainings and workshops and we will also develop a new Lesson Program. (2) The Indigenous Ag/Farmers Workshop occurred November 2022. This facilitated virtual event gathered diverse Indigenous agriculture practitioners, data science, and Indigenous Data Sovereignty scholars to discuss agriculture data priorities of Indigenous Peoples in the US. Designed and facilitated by an Indigenous led steering committee, representing 8 different tribal nations throughout the United States, participants a) identified practical uses of AI as: Monitoring, modeling, remote sensing, and to better understanding scale b) learned the application of AI and machine learning mean establishing culturally defined definitions of success and focus on center Indigenous values and c) participants left looking for more trainings, workshops, and live examples. (3) AIIRA's focus on Women in Ag and AI has grown through the continued efforts of our WIAA program. WIAA students have also 1) complete field demonstrations for undergraduate students on applications of UAVs and rovers in agricultural students, 2) discussed research, graduate education, and application of AI in agriculture with undergraduate agronomy students, 3) led a demonstration at the 4H Youth Leader Ag Innovator Experience on the insect app, UAV applications, sensors used in stress detection, and 3D/AR/VR field reconstruction to 9th through 12th graders in Iowa, 4) demonstrated the insect app, rover, and UAV to over 200 4th and 5th graders in Iowa, and 5) lead and activity at the WISE Go Further Spring 2023 event to engage and teach 8th grade students how rovers and AI are used in agriculture for insect detection and identification. AIIRA has also disseminated results through 39 publications, 43 presentations, 9 activities, 19 events, and 16 other products (listed in this report). What do you plan to do during the next reporting period to accomplish the goals?Research activities: We will continue to expand, and make progress on research activities initiated in Years 1-2. Our goal will be to wrap up a several projects by the end of Year 3, and initiate a few new projects. Selected new projects include (1) Integrate geometric and mechanics based constraints into 3D reconstruction, (2) Extend 3D geometry aware proximal sensing to hyperspectral modalities, (3) Enhance manipulation skills to incorporate domain knowledge, (4) Methods for aligning predictions with expert judgment Education and workforce development: Continue and expand on activities initiated in year 1-2, as well as start the following programmatic activities in Year 3: (1) An expanded suite of onboarding workshops including targeted education modules, (2) Regular virtual journal clubs for deep dive into key multidisciplinary papers, learning a common language, (3) Extension Showcases to Growers, (4) Start planning for an Applied AI minor at ISU. Broadening participation plans: Continue and expand on activities initiated in year 1-2: (1) Continue growing youth program with hands-on activities that explore intersection of AI and plant science in urban settings, (2) public workshops for bringing together various stakeholders and other AI Institutes, 3) explore strategic partnerships with other native nation communities, as well as minority serving institutions, 4) leverage the ExpandAI@NSF initiative to connect with MSI institutions
Impacts
What was accomplished under these goals?
Goal 1: Build plant and field scale predictive models through foundational AI advances. Accomplishments in methodologies to build the digital twins: 1) We trained AI models for insect-pest detection to classify and detect agriculturally important insect species. We integrated the model with advanced features (out-of-distribution detection and conformal predictions) to enhance robustness. 2) We developed and deployed approaches for 3D reconstruction of plants. 3) We developed and are currently testing procedural models for representing 3D phenotypes. 4) We deployed and accessed zero-shot approaches (including vision-language models) for efficient phenotyping. 5) Developed foundational advances in mathematical methods, algorithms and neural solvers for solving PDEs with a focus on creating digital twins. Accomplishments in methodologies for feedback via AI-enabled robotics: 1) Developed a pipeline for field mobile manipulation integration and corn stalk sensor insertion. 2) Continued to develop dexterous robot hand for ag applications and a versatile spectroscopic probe infrastructure. 3) Developed several frugal, portable chambers for inducing heat, drought and flooding stress. 4) We deployed a suite of co-registered multi-modal sensors on ground- and aerial- vehicles. Goal 2: Deploy plant and field scale predictive models for breeding and crop production applications. 1) We have instituted multi-pronged research activities designing and evaluating a spectrum of prediction models. Continue advancing approaches to integrate data driven and crop model based ag predictors 2) We initiated a large set of data collection campaigns this growing season. 3) Started integrating physics and digital twins with preliminary experiments in hydroponics. 4) Developing novel phenotypic descriptors for 3D plant phenotyping. 5) Deployed several AI-driven phenotyping applications. 6) Deploying AI driven phenotyping and decision support to other crops. Goal 3: Understanding and resolving social barriers to, and AI innovations for adoption of the AI technology in the agricultural ecosystem 1) We are investigating various types of feedback (on adoption of AI) from targeted audiences, including immigrant farmers and organic farmers in and around Iowa, via interviews and small group focus groups. 2) Target Market, Customer Acquisition, Social Media, and Technology adoption among Immigrant, Rural, and Small farmers: Role of field Data Compilation and AI, 3) We are continuing our data collection efforts that combine translational research and behavioral economics to understand AI adoption in agriculture, 4) We leverage a large survey instrument to perform a demographic Analysis of Iowa Farmers and their perceptions towards data governance and benefits of precision ag adoption. 5) We also are making progress in understanding the (lack of) utility of NLP explanation benchmarks, along with other foundational advances in improving trustworthiness of AI systems. Goal 4: Create a diverse, AI-aware agricultural workforce and serve as a nexus for AI-in-Ag developments: Described in detail in next section.
Publications
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
A. Nakkab, B. Feuer, C. Hegde, LiT-Tuned Models for Efficient Species Detection , AAAI AIAFS Workshop, February 2023. (Invited to Special Issue of Plant Phenomics, 2023.)
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Aditya Balu, Manoj R. Rajanna, Joel Khristy, Fei Xu, Adarsh Krishnamurthy, Ming-Chen Hsu; Direct immersogeometric fluid flow and heat transfer analysis of objects represented by point clouds, Computer Methods in Applied Mechanics and Engineering, 404(115742), 2023
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Anjana Deva Prasad, Aditya Balu, Harshil Shah, Soumik Sarkar, Chinmay Hegde, Adarsh Krishnamurthy; NURBS-Diff: A differentiable programming module for NURBS, Computer Aided Design, 146(103199), 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Anjana Deva Prasad, Anushrut Jignasu, Zaki Jubery, Soumik Sarkar, Baskar Ganapathysubramanian, Aditya Balu, Adarsh Krishnamurthy; Deep implicit surface reconstruction of 3D plant geometry from point cloud, AAAI workshop on AI for Agriculture and Food Systems (AIAFS), 2022
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Anushrut Jignasu, Ethan Herron, Talukder Zaki Jubery, James Afful, Aditya Balu, Baskar Ganapathysubramanian, Soumik Sarkar, Adarsh Krishnamurthy; Plant geometry reconstruction from field data using neural radiance fields, AAAI workshop on AI for Agriculture and Food Systems (AIAFS), 2023.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Arjun Lakshmipathy, Nicole Feng, Yu Xi Lee, Moshe Mahler, and Nancy Pollard. Artist Tools for Intuitive Modeling of Hand-Object Interactions, ACM Transactions on Graphics. 2023
- Type:
Conference Papers and Presentations
Status:
Submitted
Year Published:
2023
Citation:
B. Feuer, A. Joshi, M. Cho, K. Jani, S. Chiranjeevi, C. Deng, A. Balu, A. K. Singh, S. Sarkar, N. Merchant, A. Singh, B. Ganapathysubramanian, C. Hegde, Zero-shot Insect Detection via Weak Language Supervision , AAAI AIAFS Workshop, February 2023. (Submitted to The Plant Phenome Journal, July 2023.)
- Type:
Journal Articles
Status:
Under Review
Year Published:
2023
Citation:
B. Feuer, A. Joshi, M. Pham, C. Hegde, Distributional Robustness on a Data Budget , under review, 2023
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Carley CN, MJ Zubrod, S Dutta, AK Singh. (2022). Using machine learning enabled phenotyping to characterize nodulation in three early vegetative stages in soybean. Crop Science, 00, 1 23. https://doi.org/10.1002/csc2.20861
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
SoftTouch: A Sensor-Placement Framework for Soft Robotic Hands, in IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids), Okinawa, Japan. DOI: 10.1109/Humanoids53995.2022.10000138
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Chiteri, K.O., Chiranjeevi, S., Jubery, T.Z., Rairdin, A., Dutta, S., Ganapathysubramanian, B. and Singh, A., 2023. Dissecting the genetic architecture of leaf morphology traits in mungbean (Vigna radiata (L.) Wizcek) using genome?wide association study. The Plant Phenome Journal, 6(1), p.e20062.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Chiteri, K.O., Jubery, T.Z., Dutta, S., Ganapathysubramanian, B., Cannon, S. and Singh, A., 2022. Dissecting the Root Phenotypic and Genotypic Variability of the Iowa Mung Bean Diversity Panel. Frontiers in Plant Science, 12, p.808001.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
D. Malik, C. Igoe, Y. Li and A. Singh. Weighted Tallying Bandits: Overcoming Intractability via Repeated Exposure Optimality. International Conference on Machine Learning, ICML'23
- Type:
Other
Status:
Published
Year Published:
2023
Citation:
Soft Robotic End-Effectors in the Wild: A Case Study of a Soft Manipulator for Green Bell Pepper Harvesting. In AI for Agriculture and Food Systems (AIAFS) Workshop, AAAI Conference on Artificial Intelligence 2023, Washington, DC
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Dong, D., Nagasubramanian, K., Wang, R., Frei, U.K., Jubery, T.Z., L�bberstedt, T. and Ganapathysubramanian, B., 2023. Self-supervised maize kernel classification and segmentation for embryo identification. Frontiers in Plant Science, 14, p.1108355.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Elango D, K Rajendran, L Van der Laan, S Sebastiar, J Raigne, NA Thaiparambil, N El Haddad, B Raja, W Wang, A Ferela, KO Chiteri, M Thudi, RK Varshney, S Chopra, A Singh, AK Singh (2022). Raffinose Family Oligosaccharides: Friend or Foe for Human and Plant Health?. Frontiers in plant science, 13, 829118. https://doi.org/10.3389/fpls.2022.829118
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Herr, A. W., Adak, A., Carroll, M. E., Elango, D., Kar, S., Li, C., Jones, S. E., Carter, A. H., Murray, S. C., Paterson, A., Sankaran, S., Singh, A., & Singh, A. K. (2023). Unoccupied aerial systems imagery for phenotyping in cotton, maize, soybean, and wheat breeding. Crop Science, 63, 1722 1749. https://doi.org/10.1002/csc2.21028
- Type:
Journal Articles
Status:
Other
Year Published:
2023
Citation:
J. Hsia, D. Purthi, Z. Lipton and A. Singh. Goodharts Law Applies to NLPs Explanation Benchmarks. To Be Submitted.
- Type:
Journal Articles
Status:
Under Review
Year Published:
2023
Citation:
Jiale Feng, Mojdeh Saadati, Talukder Jubery, Anushrut Jignasu, Aditya Balu, Yawei Li, Lakshmi Attigala, Patrick Schnable, Soumik Sarkar, Baskar Ganapathysubramanian, Adarsh Krishnamurthy, 3D Reconstruction of Plants Using Probabilistic Voxel Carving, Computers and Electronics in Agriculture, Under Review, 2023.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Khanwale, Makrand A., Kumar Saurabh, Milinda Fernando, Victor M. Calo, Hari Sundar, James A. Rossmanith, and Baskar Ganapathysubramanian. "A fully-coupled framework for solving Cahn-Hilliard Navier-Stokes equations: Second-order, energy-stable numerical methods on adaptive octree based meshes." Computer Physics Communications 280 (2022): 108501.
- Type:
Journal Articles
Status:
Accepted
Year Published:
2023
Citation:
Yanbin Chang, Jeremy Latham, Mark Licht, and Lizhi Wang, "A data-driven crop model for maize yield prediction," to appear in Communications Biology, 2023.
- Type:
Conference Papers and Presentations
Status:
Awaiting Publication
Year Published:
2023
Citation:
Zheng Ni, Saba Moeinizade, Aaron Kusmec, Guiping Hu, Lizhi Wang, and Patrick S. Schnable, "New insights into trait introgression with the look-ahead intercrossing strategy," to appear in G3, 2023.
- Type:
Journal Articles
Status:
Accepted
Year Published:
2023
Citation:
Isaiah Huber, Lizhi Wang, Jerry L. Hatfield, Mark Hanna, and Sotirios V. Archontoulis, "Modeling days suitable for fieldwork using machine learning, process-based, and rule-based models," to appear in Agricultural Systems, 2023.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Zahra Khalilzadeh and Lizhi Wang, "Corn planting and harvest scheduling under storage capacity and growing degree units uncertainty," to appear in Scientific Reports, 2022.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Kar, S., Nagasubramanian, K., Elango, D., Carroll, M. E., Abel, C. A., Nair, A., Mueller, D. S., ONeal, M. E., Singh, A. K., Sarkar, S., Ganapathysubramanian, B., & Singh, A. (2023). Self-supervised learning improves classification of agriculturally important insect pests in plants. The Plant Phenome Journal, 120. https://doi.org/10.1002/ppj2.20079
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Khanwale, M.A., Saurabh, K., Ishii, M., Sundar, H., Rossmanith, J.A. and Ganapathysubramanian, B., 2023. A projection-based, semi-implicit time-stepping approach for the Cahn-Hilliard Navier-Stokes equations on adaptive octree meshes. Journal of Computational Physics, 475, p.111874.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Saeed Khaki, Hieu Pham, Zahra Khalilzadeh, Arezoo Masoud, Nima Safaei, Ye Han, Wade Kent, Lizhi Wang, "High-throughput image-based plant stand count estimation using convolutional neural networks," to appear in PLOS ONE, 2022.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Zerui Zhang, Lizhi Wang. "A look-ahead approach to maximizing present value of genetic gains in genomic selection," G3, vol. 12(8), 2022.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Saba Moeinizade, Guiping Hu, and Lizhi Wang, "A reinforcement learning approach to resource allocation in genomic selection," to appear in Intelligent Systems with Applications, 2022.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Fatemeh Amini, Guiping Hu, Lizhi Wang, and Ruoyu Wu, "The L-shaped selection algorithm for multi-trait genomic selection," to appear in Genetics, 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
M. Cho, et al, Sphynx: A Deep Neural Network Design for Private Inference , IEEE S&P, October 2023
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Nagasubramanian K, AK Singh, A Singh, S Sarkar, B Ganapathysubramanian. (2022). Plant phenotyping with limited annotation: Doing more with less. The Plant Phenome Journal, 5, e20051. https://doi.org/10.1002/ppj2.20051
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Rairdin A, F Fotouhi, J Zhang, DS Mueller, B Ganapathysubramanian, AK Singh, S Dutta, S Sarkar, and A Singh. (2022). Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean. Frontiers in plant science, 13, 966244. https://doi.org/10.3389/fpls.2022.966244
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2023
Citation:
Siva Kailas, Wenhao Luo, and Katia Sycara. Multi-robot adaptive sampling for supervised spatiotemporal forecasting. In Progress in Artificial Intelligence: 22nd EPIA Conference on Artificial Intelligence, EPIA 2023. Springer, 2023.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Webster RW, M McCaghey, B Mueller, C Groves, FM Mathew, A Singh, M Kabbage, DL Smith. (2023). Development of Glycine max Germplasm Highly Resistant to Sclerotinia sclerotiorum. PhytoFrontiers. https://doi.org/10.1094/PHYTOFR-01-23-0009-R
- Type:
Journal Articles
Status:
Accepted
Year Published:
2023
Citation:
High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants
- Type:
Other
Status:
Awaiting Publication
Year Published:
2023
Citation:
A Systematic Error Cleaning and Correction Pipeline for Field High-Throughput Phenotyping
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Young TJ, TZ Jubery, CN Carley, M Carroll, S Sarkar, AK Singh, A Singh, B Ganapathysubramanian. (2023). "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies. Frontiers in plant science, 14, 1141153. https://doi.org/10.3389/fpls.2023.1141153
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Progress 09/01/21 to 08/31/22
Outputs
Target Audience:AIIRA reaches a broad and diverse audience of USDA/NIFA stakeholders through our research and outreach activities. • K-12 students (middle and high school) including students of color and students from economically disadvantaged backgrounds • Graduate students and postdoctoral fellows • Undergraduate and community college students including women, native american and hispanic populations • Farmers and commodity groups including Indigenous Farmers • Extension and teaching professionals, Ag professionals • USDA ARS scientists • Industry Changes/Problems:Management Team: C. Lawrence-Dill (ISU) has transitioned from a management team member to an ex-officio role due to her promotion into a leadership position within Iowa State University and appointment to the FFAR Board of Directors. Now serving as the Associate Dean of Research for the College of Agriculture and Life Sciences, Lawrence-Dill has valuable experience and expertise, and she continues her contributions to AIIRA with her graduate students and their projects. AIIRA's management structure includes a succession plan that lays out the process for this transition. Her role as co-lead of the Education and Workforce Development Thrust has been transitioned to A. Johri (GMU). Other personnel: 1) J. Schnable (UNL) has taken a leave of absence to join Google X, the Moonshot Factory, for four months. We anticipate that his experience at X will be highly beneficial to AIIRA in terms of identifying opportunities and synergies. Ravi Mural will temporarily step in for J. Schnable for Fall 2022. 2) Peter Kyveryga has taken a new position outside of the Iowa Soybean Association and his role is temporarily being filled by Aaron Presholt until Kyveryga's position is filled. Kyveryga has joined John Deere and will be a useful external collaborator in future. Presholt has been tangentially involved in AIIRA broader impact efforts and the transition to fulfilling Kyveryga's responsibilities has progressed smoothly. Hiring: Hiring students and staff was a challenge due to the COVID pandemic. Visa processes for international students/staff resulted in long wait-times. We anticipate this to be a temporary issue, with rapidly shortening wait-times suggesting this is no longer an issue in year 2 and beyond. Budget: 1) Due to our Oct 1 start date, along with timing of standard administrative processes involved in subcontract issuance, we underspent our budget for Y1. We anticipate catching up to planned budget burn-rates in the next reporting cycles. 2) No-cost extension is needed to maintain graduate research assistantships from year 1 into year 2, and potentially for future years. What opportunities for training and professional development has the project provided?AIIRA's education and workforce development opportunities aim to develop talent and skills for a highly competent next generation AI workforce which includes activities for graduate and undergraduate students, and continual learners of all ages. A diverse team from across AIIRA's collaborative institutions aim to leverage existing partnerships to create new learning opportunities and expand existing programs aimed at advancing knowledge and education in AI in Ag. University and college: AIIRA personnel directly mentored 3 postdocs, 53 graduate students, and 11 undergraduates across 8 institutions. Mentoring consisted of both formal course work, training and research assistantship, as well as informal networking across institutions. We briefly detail selected activities below, with additional details in the full report to the cognizant program manager. Undergrad/grad education: 1) We made progress planning for undergraduate programming in AI in Ag. The AI for Robotics course developed at CMU forms the basis for a proposed four course AI in Ag sequence that will be expanded at other AIIRA institutions. 2) GMU has prepared a case study course on ethics of use in AI for Ag that is deploying this fall. 3) Additionally, AIIRA faculty served as guest lecturers for CMU's new Robotics and AI for Ag course in Spring 2022. 4) ISU has developed an undergraduate minor in engineering called Cyber-Physical Systems with modules planned for AI in Ag. 5) We are planning for Ag-AI content in the recently offered Data Science minor at ISU, focused on undergrads from Ag and Life Sciences. 5) New course on "foundations in deep learning" at NYU. One of the first course of its kind nationwide. Workforce development via Ag-AI related training opportunities: 1) Application development exploring various AI and software tools with three honors freshmen, one of which built a prototype for their summer research. 2) AI, digital transformation and digital agriculture research involving one honors undergraduate and an MBA graduate student, 3) Student mentoring on entrepreneurial marketing projects in digital agriculture, agricultural and applied economics as it relates to ethical and social barriers of AI in Ag. 4) Four graduate and two undergraduate students worked on different aspects of AI, AI Ethics and AI ethics and agriculture related projects resulting in created case studies for instruction implemented in an undergraduate course in Fall 2022. 5) The development of ML algorithms by two undergraduate students, who took into account interaction with humans and the capabilities of human feedback. 6) Exposed a sustainable agriculture & sociology PhD student to the social dimensions of AI in Ag literature and research. 7) Several engineering undergrads exposed to AI-in-Ag research opportunities. 8) Engineering capstone design teams. Workforce development via connecting with stakeholders: AIIRA completed activities to generate a new AI-in-Ag workforce. Efforts were focused on identifying means to connect with farmers and growers to demonstrate AI driven tools and technologies as well as means to showcase AI driven technologies to the broader agricultural community. 1) Workshop on AI, Ag, Ethical and Responsible Development which attracted participants from the industry, postdocs and students from other AI & AG institutes, and other participants from academia and non-profits. 2) AIIRA presence at the 2022 Farm Progress Show in Boone, IA. 3) The Data Science for the Public Good, Wholesale Local Food Benchmarking Project sponsored by AIIRA and managed by a graduate fellow and three undergraduate interns. The DPSG students learned and practiced data discovery and practical applications of AI in Ag. Workforce development via community building activities: Organized several workshops to disseminate research and education advances to various stakeholders. 1) Tutorials on using HPC with machine learning at the CVPR conference and the SuperComputing conference, 2) Workshop on Scientific Machine Learning at ISU, 3) International Workshop on Machine Learning for Cyber Ag systems (MLCAS) in 2021 (virtual), and scheduled for 2022 (Ames, IA), 4) AAAI workshop on AI for Ag and Food Systems (virtual), 5) Presented AIIRA vision and collaborative opportunities at various venues (USDA SAS PI meeting, AG2PI Field days, Farm Progress Show, National Association for Plant Breeders (NAPB) meetings, NSF ERC IoT4Ag seminar series, State of Iowa Legislators, IA Board of Regents, US & Mexico Secretaries of Ag). Pre-college education: 1) AIIRA's pre-college education efforts focused on leveraging existing partnerships at CMU with a goal to develop a new curriculum in Pittsburgh, PA through the Urban Robotony workshop for middle schoolers. As a signature AIIRA effort, the program is a hands-on program targeting middle school students that combines AI, robotics, and plant science. The first year of the program was dedicated to building partnerships, selecting a hardware platform and developing curriculum for the pilot, which was offered in August 2022. The pilot took the form of a 1-day, 3-hour workshop offered at the Drew Matheison Greenhouse in Pittsburgh and included an introduction to plant science, AI technologies for Ag, and a hands-on activity using the FarmBot platform. There were 45 students in attendance, including 9-12th grader students and students in Drew Matheison's Horticulture Technology workforce development program. 2) ISU's summer internships resulted in five high school students learning machine learning coding as it relates to field corn and common agricultural insects. AIIRA is sponsoring a unique informal learning opportunity to engage with undergraduate students outside of the AI and Agriculture frames. A. Johri (GMU) and a graduate student are planning a writing competition to bring a diverse audience to generate ideas for AI solving agricultural problems. Beginning in August 2022, undergraduates across the United States are invited to submit writings that imagine the world in the year 2050 and explore the relationship between humans, technology, and their food. The goal is for this opportunity to expand in the future to attract a broader audience to AI-in-Ag and generate further creative solutions to today's problems. How have the results been disseminated to communities of interest?AIIRA results are disseminated through collaboration and knowledge transfer workshops and training, sharing of benchmark datasets, and participation in field days to engage directly with stakeholders. Collaboration and knowledge transfer: AIIRA has worked towards creating a learning community to support a collaborative research and training environment across the transdisciplinary boundaries of team members. AIIRA is administratively housed within the Center for Translational AI (TrAC) at ISU. With the goal of providing the means to support collaboration and sharing of knowledge, and using team science based approaches, the following workshops and trainings have been established or shared: AIIRA sponsored workshops and training: AIIRA sponsored, developed and implemented several workshops and training opportunities to AIIRA stakeholders. 1) TrAC Journal Club included AIIRA graduate students who participated in 7 sessions offered in Spring 2022. 2) TrAC Seminar Series (offered virtually) sponsored by AIIRA presented to a wide audience across disciplines including a) Oliver Kroemer of Carnegie Mellon University (CMU) Robotics Institute (Jan 28, 2022), b) Mark Ryan of Wageningen Economic Research (Feb 25, 2022), c) Volkan Isler of University of Minnesota/Samsung Research (April 15, 2022), d) Brian Bailey of University California Davis, presenting on Helios (May 5, 2022), 3) Fundamentals of Deep Learning with Multi-GPUs - in collaboration with NVIDIA Deep Learning Institute (July 29). 4) Third International Workshop of Machine Learning for Cyber-Agricultural Systems (MLCAS2021) workshop (November 2 - 4, 2021) aimed to bring together academic and industrial researchers and practitioners in the fields of machine learning, data science and engineering, plant sciences and agriculture, in the collaborative effort of identifying and discussing major technical challenges and recent results related to machine learning-based approaches. 5) Intro to Cloud Based Deep Learning, in collaboration with CyVerse, offered an introduction to concepts in cloud, containerization, and GPU computing with Python. 6) APSIM Informational Webinar presented by S. Archontoulis on the Agricultural Production Systems sIMulator (APSIM) and how the cropping systems model is capable of simulating the growth and development of many crops on a daily basis. 7) FarmBot Workshop (Urban Robotany) by George Kantor at the Carnegie Mellon University (CMU) Robotics Institute offering an introduction to plant science, AI technologies for Ag, and a hands-on activity using the FarmBot platform (August 18, 2022). Workshops and training promoted to AIIRA: The following opportunities were shared broadly to further developing cross disciplinary, cross institutional relationships and to further the mission of AIIRA. 1) Plant Phenomics Phridays - Held on Fridays at ISU, Speakers in this series describe novel solution approaches to plant science problems from different perspectives spanning the plant sciences, statistics, data sciences and engineering. 2) International Conference on Digital Technologies for Sustainable Crop Production (DIGICROP), March 28-30, 2022 - AIIRA team members participated in the opportunity to develop, propose, use or evaluate new digital technologies across the intersection of engineering, robotics, crops sciences, computer science, agricultural sciences, economics and phenotyping. 3) Virtual Collaborative Workshop for Early-Career Academics via the KY EPSCoR office, May 16, 2022 - opportunity shared to learn more about leadership style and how to build programs to expand the impact of research. 4) ICRA 2022 Workshop on Innovation in Forestry Robotics: Research and Industry Adoption, May 23, 2022 - addressing recent advances in robotics research and development for forestry applications. 5) AI FARMS - AI Foundry for Agricultural Applications Summer School - July 11-16th AIIRA graduate students and postdocs were encouraged to apply to increase their ability to engage in conversations and generate new ideas for AI applications to solve agricultural issues with AI. 5) CyVerse - Webinar: Zero to Web App: Rapid Customized Web Interfaces for Your ML Applications with Streamlit, August 26, 2022, Virtual, designed to learn how to use Streamlit, a low-code solution that allows you to quickly build web applications using Python scripts and How you can incorporate machine learning into your analysis workflow. A significant effort has been made to connect AIIRA faculty, researchers and graduate students across disciplines. Bringing 12 CMU team members from the Robotics Institute to Ames, IA for two weeks generated opportunities for the team to more easily understand the context in which they work. Having the robotics team test robots and drones in the field was highly beneficial to the research and knowledge sharing. Field Days: Engaging AIIRA's scientists, engineers and social scientists through well established, outward facing, field days and training workshops are key to identifying the needs of our stakeholders and toward disseminating AI advances and impacts to agriculture. 1) Iowa Soybean Association Tours - leveraged through ISA and Iowa Soybean Research Center, AIIRA faculty and students attended a field tour of Kemin Industries, Beck's Hybrids, and Salin247 to receive an overview of their research and test plots. 2) Farmbot Workshop [CMU] - Drew Matheison Center greenhouse, August 16 - 18, 2022, 3) 2022 Farm Progress Show, Boone IA, August 30 - Sept 1, 2022. Broadening Participation: AIIRA aims to apply best practices for mentoring and engaging women, Hispanics, and Native Americans in AI using evidence-based strategies across these underrepresented populations. (1) AIIRA, in collaboration with the Native Nations Institute at University of Arizona is developing a Software Carpentry based learning material for ML/AI instruction with an Indigenous data focus covering topics including Tribal Institutional Review Boards, The CARE Principles for Indigenous Data Governance, framework/s for data sharing, cultural autonomy, and the importance of consent in data. The global platform aligns with CELSI (Cultural, Ethical, Legal, and Social Implications) format to allow for content that is broader and outside of the scope of Indigenous topics. (2) In addition to the lesson program and courses, we will also have team members complete the Carpentiers Instructor Training and we will write a scholarly manuscript detailing the co-production of the new lesson program. (3) The Indigenous Ag/Farmers Workshop is in the planning stages for November 2022. The one-day facilitated, interactive event will be guided by a 4-5 member, scholar/practitioner Indigenous Steering Committee to structure the workshop to identify issues, opportunities and strategies for meeting Indigenous agricultural data needs. Workshop attendees will consist of industry leaders and Indigenous community members. (4) AIIRA's focus on Ag-Women-in AI has been highlighted in a recent video "Bots in Beans: Ag Women using AI Tools (Robots and UAVs in Ag)" featuring graduate Student Ashlyn Reardon (ISU) (https://youtu.be/PLfgCVFCeCY). (5) AIIRA has generated 270 organic followers across 3 platforms (Twitter, LinkedIn, and Instagram) since their creation in February 2022. Posts shared include unique AIIRA content and sharing of AIIRA and AIIRA Institution related themes. AIIRA has also disseminated results through 24 publications, 42presentations, 15 invited talks, 4 activities, 14 events, and 2 other products (listed in this report). What do you plan to do during the next reporting period to accomplish the goals?Research activities: We will continue to expand, and make progress on research activities initiated in Year 1. We will start the following additional activities in Year 2: (1) Integrate geometric and mechanics based constraints into 3D reconstruction, (2) Extend 3D geometry aware proximal sensing to hyperspectral modalities, (3) Design and deploy strategies for multi-modal data assimilation. (4) Enhance manipulation skills to incorporate domain knowledge, (5) Scale up to fieldable intelligent systems, (5) Enhance the capability of the production Ag decision support system, (6) Design and implement nationwide online surveys with a wide spectrum of farmers to elicit potential motivators and barriers for adopting new technologies with AI components. (7) Methods for aligning predictions with expert judgment, (8) Methods for robustification, and secure training of AI-enabled models. Education and workforce development: Continue and expand on activities initiated in year 1, as well as start the following programmatic activities in Year 2: (1) On boarding workshops including targeted education modules, (2) Weekly Seminars to network and learn the state-of-art landscape, (3) Weekly virtual journal clubs for deep dive into key multidisciplinary papers, learning a common language, (4) data science and other citizen science competitions for bringing together AI and Ag experts to energize the future workforce, (5) Ag-AI Graduate Minor Development at Iowa State, (8) Extension Showcases to Growers. Broadening participation plans: Continue and expand on activities initiated in year 1, as well as start the following programmatic activities in Year 2: (1) Establish youth program with hands-on activities that explore intersection of AI and plant science in urban settings, (2) public workshops for bringing together various stakeholders and other AI Institutes, 3) explore strategic partnerships with other native nation communities, as well as minority serving institutions.
Impacts
What was accomplished under these goals?
Overall Impact Statement: We have made good progress in Year 1 towards accomplishing project objectives in each of thrusts. We have made foundational and translational advances impacting breeding and production agriculture; Our broadening participation, education and workforce development, and collaboration and knowledge transfer activities have already benefited a wide spectrum of stakeholders. Goal 1: Build plant and field scale predictive models through foundational AI advances. Accomplishments in sensor design and field deployment to feed the digital twins: 1) We developed a wearable, minimally invasive biosensor for quasi-continuous monitoring of salicylic acid in plant tissues. 2) We developed a wearable plant sensor that can monitor volatile organic compounds emission (specifically, methanol) directly on the leaf of a plant under field conditions with low cost and easy installation. 3) We redesigned the Episcan color sensor to capture direct and indirect light transport to image the surface and subsurface structures. Accomplishments in methodologies to build the digital twins: 1) We trained AI models for insect pest detection to classify agriculturally important insect-pesta. We also deployed this model into a user-friendly app for maximizing adoption and democratized use. 2) We developed and deployed two approaches for 3D reconstruction of plants: a) designed probabilistic approaches to 3D reconstruction from 2D multi-view images. b) reconstructed watertight STL surfaces of plants from 3D point cloud data. 3) We started development on detailed simulations of air flow, temperature and CO2 distributions at the plant scale by incorporating realistic 3D structures into fluid dynamics (CFD) simulations. Accomplishments in methodologies for feedback via AI-enabled robotics: 1) We designed and performed preliminary testing of a robotic end-effector for inserting nitrate sensors into corn stalks. 2) We developed and are currently testing methods for identifying parts of plants (e.g., leaves vs. stem) from vibrotactile feedback. 3) We deployed a suite of co-registered multi-modal sensors on ground- and aerial- vehicles. 4) We designed and performed preliminary testing of a soft robotic arm combined with a cutting tool for an initial attempt to harvest pepper plants in the field. Goal 2: Deploy plant and field scale predictive models for breeding and crop production applications. ML based vs process based vs hybrid crop models: We have instituted multi-pronged research activities designing and evaluating a spectrum of prediction models: 1) We made progress towards a new software system to run the APSIM crop model in parallel. The new software feeds the APSIM crop model with weather data, management data and soil data for every grid cell. Once complete, we will be able to run APSIM across scales, from subfield to field and to regional scale. 2) We built deep learning, as well as statistical models to extract phenotypic information from various field experiments (maize, soybean). These data were used to train yield prediction ML algorithms. We evaluated the ability to predict not only yield, but also other traits such as protein, and oil concentrations. 3) We built a hybrid model for crop yield prediction that combines process based crop models with data driven machine learning algorithms. As a follow up of this activity, we are starting to validate this model in hydroponic experiments. Optimizing Breeding Strategies: 1) We designed new algorithms for trait introgression, which challenges the widely used backcrossing breeding scheme with a more general and more effective intercrossing strategy. 2) We designed a breeding simulator and used it to organize a class competition. We plan to open source the simulator in three popular programming languages: Matlab, Python, and Julia. Farm management decision support tools: 1) Developed a simulator for a virtual farm with potential biotic stresses and stress mitigation strategies based on available knowledge in the literature and crop models. Developed an ML based decision support tool for optimizing management strategies using this simulator. 2) We considered an abstraction of the farming decision problem as an AI-enabled sequential decision-making-process. We leverage the specific memory-bounded structure and provide an algorithm along with its complete policy regret. We couple this result with a lower bound on the complete policy regret. Data collection campaigns: We have initiated a large set of data collection campaigns this growing season. 1) Microbe assisted selection for identifying heat tolerant genes and phenes in soybean. We conducted a factorial experiment with four soybean genotypes, with and without microbes, and heat versus optimum temperature. Experiments were performed in the Enviratron (a controlled environment facility at ISU) and we collected plant tissues for DNA, RNA, root exudate, and anatomical studies. 2) Heat stress tolerance screening to identify candidate genes using a large panel of soybean accessions. We screened 450 unique soybean genotypes for their tolerance to heat stress. 3) Development of field deployable greenhouse for heat tolerance screening. We partnered with two teams in a senior capstone class in engineering to develop portable and autonomous greenhouses for use in the field. This is to allow for simulation of heat stress conditions out in the field. 4) We collected temporally and spatially tagged multimodal data (drones, ground measurements, and high resolution satellite) across the geographically distributed, highly instrumented tests sites available through the USDA-NIFA High-Intensity Phenotyping Sites (HIPS) project, which is evaluating hundreds of maize inbreds and hybrids. Goal 3: Understanding and resolving social barriers to, and AI innovations for adoption of the AI technology in the agricultural ecosystem Social science activities: 1) Fielded a survey in 2022 among Iowa farmers which included a module on precision agriculture technology adoption. Conducted extensive original data analysis of survey data containing measures of precision agriculture adoption among Iowa farmers that was collected in 2017. 2) Built a software prototype to convert voice transcripts of the operational tasks from vegetable produce farner into a knowledge graph using AI and NLP. 3) Completed qualitative data analysis for a study on the digital agriculture ecosystem. 4) Analysis of data on digital agriculture technology and networking collected via in-person surveys with commodity farmers in Iowa in 2021 and 2022. 5) Creating marketing knowledge on how AI impacts relational dynamics amongst digital agriculture stakeholders and consumer behavior. AI activities: 1) Two new frameworks for designing AI models that respect privacy of stakeholders in the data processing pipeline, thereby enhancing end user trust. This work represents a significant improvement over the current state of the art. 2) Formulated and deployed out-of-distribution algorithms for insect-pest classification. This allows the model to abstain from prediction in a robust manner, further enabling end user trust. 3) We developed a new approach for interrogating and enhancing robustness of a particular family of neural networks called vision transformers (ViTs) which are the current state of the art. 4) We are investigating various types of feedback that experts can provide and their value for an AI system. Goal 4: Create a diverse, AI-aware agricultural workforce and serve as a nexus for AI-in-Ag developments: Described in detail in next section.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Leikas, J., Johri, A., Latvanen, M., Wessberg, N. & Hahto A. (2022). Governing Ethical AI Transformation: Case Aurora AI. Frontiers in Artificial Intelligence: AI for Human Learning and Behavior Change.
- Type:
Other
Status:
Accepted
Year Published:
2021
Citation:
Cho, M., Nagasubramanian, K., Singh, A.K., Singh, A., Ganapathysubramanian, B., Sarkar, S., Hegde, C. (2021) Privacy-Preserving Deep Models for Plant Stress Phenotyping
- Type:
Other
Status:
Accepted
Year Published:
2021
Citation:
Kar, S., Nagasubramanian, K., Elango, D., Nair, A., Mueller, D.S., ONeal, M.E., Singh, A.K., Sarkar, S., Ganapathysubramanian, B., and Singh, A. (2021) Self-Supervised Learning Improves Agricultural Pest Classification
- Type:
Other
Status:
Accepted
Year Published:
2021
Citation:
Chiranjeevi, S., Young, T., Jubery, T.Z., Nagasubramanian, K., Sarkar, S., Singh, A.K., Singh, A., and Ganapathysubramanian, B. (2021). Exploring the use of 3D point cloud data for improved plant stress rating
- Type:
Other
Status:
Accepted
Year Published:
2021
Citation:
Nagasubramanian, K., Singh, A.K., Singh, A., Sarkar, S., and Ganapathysubramanian, B. (2021). Plant Phenotyping with Limited Annotation: Doing More with Less
- Type:
Conference Papers and Presentations
Status:
Awaiting Publication
Year Published:
2022
Citation:
Bauer, Dominik, Cornelia Bauer, Arjun Lakshmipathy, Roberto Shu, and Nancy S. Pollard. "Towards Very Low-Cost Iterative Prototyping for Fully Printable Dexterous Soft Robotic Hands." International Conference on Soft Robotics (ROBOSOFT) 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Fully Printable Low-Cost Dexterous Soft Robotic Manipulators for Agriculture,
D Bauer, C Bauer, A Lakshmipathy, N Pollard - AI for Agriculture and Food Systems, 2021
- Type:
Conference Papers and Presentations
Status:
Accepted
Year Published:
2022
Citation:
Lakshmipathy, Arjun, Dominik Bauer, Cornelia Bauer, and Nancy S. Pollard. "Contact Transfer: A Direct, User-Driven Method for Human to Robot Transfer of Grasps and Manipulations." IEEE International Conference on Robotics and Automation (2022).
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Johri, A. (2022). Augmented Sociomateriality: Implications of Artificial Intelligence for the Field of Learning Technology. Research in Learning Technology.
- Type:
Other
Status:
Published
Year Published:
2022
Citation:
Johri, A. & Hingle, A. (2022). Learning to Link Micro, Meso, and Macro Ethical Concerns Through Role-Play Discussions. Proceedings of FIE 2022.
- Type:
Other
Status:
Published
Year Published:
2022
Citation:
Hingle, A. & Johri, A. (2022). Assessing Engineering Students Representation and Identification of Ethical Dilemmas through Concept Maps and Role-Plays. Proceedings of ASEE 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Dhruv Malik; Yuanzhi Li; Aarti Singh. Complete Policy Regret Bounds for Tallying Bandits. Proceedings of Conference on Learning Theory (COLT), 2022.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Ibraham, H., Satyanarayana, M., Schnable, P., & Dong, L. (2022). Wearable plant sensor for In Situ monitoring of volatile organic compound emissions from crops. American Chemical Society https://doi.org/10.1021/acssensors.2c00834
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Ansarifar J, Wang L, Archontoulis SV, 2021. An interaction regression model for crop yield prediction. Nature Scientific Reports 11, 17754.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Shahhosseini M, Hu G, Khaki S, Archontoulis SV, 2021. Corn yield prediction with ensemble CNN-DNN. Frontiers Plant Science 12, 709008. doi: 10.3389/fpls.2021.709008
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Akhavizadegan F, Wang L, Huber I, Archontoulis S, 2021. A Time-Dependent Parameter Estimation Framework for Crop Modeling. Nature Scientific Reports 11:11437
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Standardized genome-wide function prediction enables comparative functional genomics: a new application area for Gene Ontologies in plants L Fattel, D Psaroudakis, CF Yanarella, KO Chiteri, HA Dostalik, P Joshi, ... GigaScience 11 https://doi.org/10.1093/gigascience/giac023
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Jayashankar, P, Johnston, W., and Nilakanta, S., How do agricultural stakeholders perform institutional work through AI? Macro-marketing Conference, 2021.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Jayashankar, P, Johnston, W., and Nilakanta, S., Market-shaping through B2B value chain reconfiguration a study of digital twins, CBIM International Conference, 2021.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Hegde, C., Cho, Ghodsi, Reagen, Garg. Sphynx: ReLU efficient network design for Private Inference , IEEE S&P, 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Hegde, C., Cho, Joshi, Reagen, Garg. Selective Network Linearization for Private Inference , ICML 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Hegde, C., Cho, Singh, Singh, Sarkar, Koushik N., Ganapathysubramanian. Privacy-preserving Deep Models for Plant-Stress Phenotyping , AAAI Workshop, AIAFS 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Hegde, C., Joshi, Jagatap. Adversarial Token Attacks on Vision Transformers , CVPR 2022 Workshop on Transformers for Vision.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Singh, A., Saadit, Ganapathysubramanian. OOD Algorithms for Robust Insect-Pest Classification
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Zhou, Y., Kusmec, A., Mirnezami, S.V., Attigala, L., Srinivasan, S., Jubery, T.Z., Schnable, J.C., Salas-Fernandez, M.G., Ganapathysubramanian, B. and Schnable, P.S., 2021. Identification and utilization of genetic determinants of trait measurement errors in image-based, high-throughput phenotyping. The Plant Cell, 33(8), pp.2562-2582.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Chiteri, K.O., Jubery, T.Z., Dutta, S., Ganapathysubramanian, B., Cannon, S. and Singh, A., 2022. Dissecting the root phenotypic and genotypic variability of the Iowa mung bean diversity panel. Frontiers in plant science, 12, p.808001.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Jubery, T.Z., Carley, C.N., Singh, A., Sarkar, S., Ganapathysubramanian, B. and Singh, A.K., 2021. Using machine learning to develop a fully automated soybean nodule acquisition pipeline (snap). Plant Phenomics, 2021.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Riera, L.G., Carroll, M.E., Zhang, Z., Shook, J.M., Ghosal, S., Gao, T., Singh, A., Bhattacharya, S., Ganapathysubramanian, B., Singh, A.K. and Sarkar, S., 2021. Deep multiview image fusion for soybean yield estimation in breeding applications. Plant Phenomics, 2021.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Shook, J., Gangopadhyay, T., Wu, L., Ganapathysubramanian, B., Sarkar, S. and Singh, A.K., 2021. Crop yield prediction integrating genotype and weather variables using deep learning. Plos one, 16(6), p.e0252402.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Guo, W., Carroll, M.E., Singh, A., Swetnam, T.L., Merchant, N., Sarkar, S., Singh, A.K. and Ganapathysubramanian, B., 2021. UAS-based plant phenotyping for research and breeding applications. Plant Phenomics, 2021.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Kusmec, A., Zheng, Z., Archontoulis, S., Ganapathysubramanian, B., Hu, G., Wang, L., Yu, J. and Schnable, P.S., 2021. Interdisciplinary strategies to enable data-driven plant breeding in a changing climate. One Earth, 4(3), pp.372-383.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Mirnezami SV, Srinivasan S, Zhou Y, Schnable PS, Ganapathysubramanian B. Detection of the progression of anthesis in field-grown maize tassels: A case study. Plant Phenomics. 2021 Mar 3;2021.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Nagasubramanian, K., Jubery, T., Fotouhi Ardakani, F., Mirnezami, S.V., Singh, A.K., Singh, A., Sarkar, S. and Ganapathysubramanian, B., 2021. How useful is active learning for image?based plant phenotyping?. The Plant Phenome Journal, 4(1), p.e20020.
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