Source: UNIVERSITY OF ILLINOIS submitted to
AI INSTITUTE: ARTIFICIAL INTELLIGENCE FOR FUTURE AGRICULTURAL RESILIENCE, MANAGEMENT, AND SUSTAINABILITY (AIFARMS)
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
EXTENDED
Funding Source
Reporting Frequency
Annual
Accession No.
1024178
Grant No.
2020-67021-32799
Project No.
ILLU-000-637
Proposal No.
2020-09148
Multistate No.
(N/A)
Program Code
A7303
Project Start Date
Sep 1, 2020
Project End Date
Aug 31, 2023
Grant Year
2021
Project Director
Adve, V.
Recipient Organization
UNIVERSITY OF ILLINOIS
2001 S. Lincoln Ave.
URBANA,IL 61801
Performing Department
Department of Computer Sciences
Non Technical Summary
In the recent past, especially the last half of the twentieth century, advances in irrigation, fertilization, mechanization, and breeding have helped agriculture keep pace with the growth in world population. This success, however, has come at enormous costs to the environment and human health: due to overuse of chemicals and antibiotics, soil degradation and erosion, increased herbicide resistance, fertilizer runoff, etc. Today, we are faced with the challenge of feeding an additional 2-3 billion people by 2050, despite labor availability and arable land decreasing and environmental constraints increasing. Conventional, as-practiced technologies cannot solve these looming fundamental challenges in a sustainable manner.Advances in the theory and practice of Artificial Intelligence (AI) play an important role in addressing these challenges in two key ways. First, improvements in autonomy and human-augmented systems can increase agricultural output while also reducing harmful environmental impact, with fixed or even slightly reduced labor requirements, e.g., by allowing far more detailed monitoring and decision-making in large-scale production of both crops and livestock. Second, AI research can yield new techniques for decision-making based on agricultural data, which comes from highly diverse sources, and which spans increasingly wide spatial scales from individual plants to whole regions and widely varying time scales from seconds to entire seasons.The AIFARMS Institute will develop and demonstrate the advances in AI necessary to address the major challenges facing world agriculture, by combining basic research, novel experimental facilities, state-of-the-art data analytics, and eventually evaluation in production settings. Farmers will be involved in all stages of the work, from identifying practical constraints on technological solutions to evaluating them in the field and providing feedback on the incentives and barriers to their adoption. The Institute will work with technology and agriculture companies to transfer the outcomes of the research into production use. Multidisciplinary educational programs spanning Computer Science and Agriculture, with increased AI content, will develop the skilled workforce needed to develop, deploy and support these advanced technologies in complex, modern agricultural operations. Education and farmer training programs will be carefully designed to increase participation of underrepresented and disadvantaged groups in both agriculture and relevant STEM disciplines.If AIFARMS is successful, it will lead to technological advances that enable farmers to increase food production and profitability in the long term, to do so without worsening environmental harms, to be much more resilient to climate change, and to absorb steadily declining agricultural labor. It will lead to foundational advances in AI that will have broad benefits in many other areas. It will also lead to a more diverse agriculture and technology workforce.
Animal Health Component
15%
Research Effort Categories
Basic
60%
Applied
30%
Developmental
10%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
4027410208020%
1020110100015%
2031510106015%
3073510208015%
6011510301015%
4047410208020%
Goals / Objectives
The broad goals of this project are to achieve foundational advances in AI by developing innovative techniques and use them to address pressing challenges in agriculture. The project will tackle four key agricultural challenges: (1) using low-cost autonomous systems for greater sustainability (increased productivity and reduced environmental impact) and labor efficiency. (2) improving livestock management through AI-driven monitoring, recognition, and behavioral analysis; (3) designing sustainable and climate-resilient agricultural systems through the application of new AI techniques capable of forecasting best management practices under increasing climate variability and change and across widely varying scales; and (4) reducing the risk and uncertainty of crop production using an AI-based predictive framework to target and tailor input with variable rate applications.The specific objectives required to achieve these goals are as follows:1. Develop new approaches for six foundational AI research goals: learning from limited data, heterogeneous information fusion, integrating domain knowledge into machine learning (ML), federated learning at the edge, learning for control, and human-in-the-loop intelligence.2. Develop AI-based techniques to enable autonomous teams of low-cost, mobile robots to perform a variety of agricultural tasks.3. Enable semi-autonomous monitoring of livestock behavior, using sensors together with novel AI techniques.4. Use novel learning and heterogeneous data-driven prediction for more effective high-throughput phenotyping and predictive statistical and process-based models.5. Develop novel machine learning algorithms trained on extensive experimental data and on validated crop simulation models to enable detailed predictions of crop yield, soil water, carbon, and nutrient fluxes across spatial scales in the US Midwest.
Project Methods
The project is designed around tackling key agricultural challenges using novel AI-based techniques, and carrying out the foundational AI research needed to make those techniques successful. The foundational AI research goals include learning from limited data, heterogeneous information fusion, integrating domain knowledge into machine learning (ML), federated learning at the edge, learning for control, and human-in-the-loop intelligence.The agricultural challenges and the AI techniques required for them are divided into four synergistic thrusts. Thrust 1 concerns advancing AI for enabling autonomous farming. This includes advancing learning for navigation and perception with low-cost sensors, reinforcement learning for plant manipulation, federated learning across robots and fields, adaptive communication networks in bandwidth limited agricultural fields, and human-robot interaction methods for intuitive farmer-robot interaction. Thrust 2 concerns advancing AI for optimizing labor in livestock and to enable more autonomy in livestock management. The tasks here include AI for objective and activity recognition from dense video data, processing large volumes of video data on the farm to identify key events and trigger human involvement as needed, and advancing AI to get reliable outputs with low-cost sensors. Thrust 3 is focused on advancing AI for environmental resilience. Tasks here include advancing AI for obtaining reliable predictions from multi-modal, partially labeled, and noise data; identification of critical phenotypes and variables from data from mobile and stationary sensors. Thrust 4 is focused on advancing AI for improving soil health in agriculture. The key tasks here include accurate modeling from sparse geospatial training data, and advancing transfer learning to fill in data gaps.The project will expand and use several innovative and unique experimental facilities to develop and evaluate proposed solutions. These include an autonomous "farm of the future," the SoyFACE facility for evaluating climate change impacts under open-air production conditions, the Mockler Lab for advanced computational bioinformatics and molecular genomics, the Piglet Nutritional and Cognitive Lab, the Thoreau soil sensor networks, and new experimental microsites at Tuskegee University.The project will also assemble substantial datasets for key challenge problems for agriculture. Investigators will work together to combine data from experiments in autonomous agricultural systems, AI-augmented livestock monitoring, genomics and breeding for environmental resilience, and soil health monitoring.

Progress 09/01/21 to 08/31/22

Outputs
Target Audience:Research in AIFARMS has been disseminated to various target audiences, we have published several papers in peer reviewed journals and made presentations at professional meetings attended by other academic scientists. Through their engagement in AIFARMS research, undergraduate and graduate students as well as postdoctoral fellows are receiving training. The AIFARMS team has been reaching farmers, Extension agents and industry through media interviews, podcasts, demonstrations and field days. We have also targeted the public as an audience for our activities through appearances on the local TV and the Farm Progress sShow. AIFARMS has been featured on several national media outlets, broadening our target audience to include members of the general public. We are collaborating with several companies with an interest in our research to demonstrate and test practical applications and adapt our approaches to meet their needs. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?In addition to the items illustrated in section Products / Other Products, we would like to highlight the following opportunities provided by AIFARMS: Students and post-docs: 45 students and post-doctoral scientists work on AIFARMS projects, spread out across all the AIFARMS research thrusts, with several of them working between agriculture and AI disciplines and between thrusts. Tenure-track faculty and senior researchers: Of the 43 senior researchers working with AIFARMS, 38 are tenure-track faculty or senior researchers, spread out across the six thrusts. There is strong interaction among the tenure track faculty from disparate areas--soil science, plant biology, animal science, human- centered design, and computer sciences as joint projects have begun. This has enabled a new degree of necessary interdisciplinarity to help researchers involved in agriculture going forward. Monthly seminars: Information is disseminated via monthly seminars across AIFARMS and individual thrust meetings which are held weekly or biweekly for each thrust. These thrust meetings are often attended by students and post-docs who gain valuable experience in the running of complicated, multi-faceted projects. Development of new Masters of Engineering Degree for Digital Agriculture: In Year 2 AIFARMS, in conjunction with The Center for Digital Agriculture, received approval for the Master of Engineering in Digital Agriculture degree, and the first cohort of students will be admitted for the spring 2023 semester. The program's online delivery aims to provide continued training for working professionals, international students, and anyone else interested in digital ag. The Digital Ag non-transcriptable certificate program has also been approved, and proposals for additional certificate themes are pending approval. Outreach: Outreach impacts training and professional development in both directions. This year there have been several outreach efforts with AIFARMS researchers participating in farmer focus groups, livestock farm visits, and related events. Thrust 5 conducted two farmer focus groups to determine the incentives, barriers and concerns related to adoption of mechanical weeding and cover cropping with and without autonomous robotic systems. These interactions have helped the training of farmers in techniques developed by AIFARMS researchers and helped AIFARMS researchers' professional development from insights gained from non-academic customer environments. Recruiting diverse students: The AIFARMS REU program, which has now run successfully for two years in a row, focused primarily on minority students from 1890 institutions, as described below. Moreover, several AIFARMS team members work closely with the National Society of Minorites in Agriculture, Natural Resources and Related Sciences (MANRRS). Dr. Bolden-Tiller has served as MANRRS president and is currently very active within the organization. Many of the outreach and development opportunities, including the FarmBot and CS teacher training activities, serve underrepresented minority populations, including those in Chicago and the Metro East St. Louis region of Illinois. iCAN program: iCAN was designed to addresses both the talent gap in tech and the underrepresentation of these groups in CS. The iCAN program provides foundational training in CS to agriculture students without said training at Illinois and partner institutions. The program is aimed at students interested in professional development or obtaining a graduate degree in the Digital Agriculture program, thus bringing more students with agriculture backgrounds into computing. REU and undergraduate internships: AIFARMS funded 12 summer REU interns in 2022 to work within research thrust 1, 2, 3, and 4. These students receive a stipend, as well as housing and meals for two months. Nine students are from historically black colleges and universities, which include Tuskegee University and Langston University. Additionally, four students are supported with scholarships from Microsoft and Corteva industry partners. AI Foundry for Agricultural Applications Summer Workshop: Dr. Angela Green-Miller and Dr. Isabella Condotta, members of thrust 2 (Livestock Management), hosted a summer workshop and Hackathon event. Students participated in lectures and virtual activities on topics focused on AI and ML in agriculture applications. Students were mentored by faculty from the Departments of Agricultural and Biological Engineering, Animal Sciences, and industry partners. The program aimed to teach skills relevant to many agricultural applications, and the datasets used in the course were specific to livestock systems. On the last two days of the course, participants were challenged to develop a solution to a digital agriculture problem in an inspiring Hackathon. How have the results been disseminated to communities of interest?Engagement with the Research Community: Results have been disseminated to academic communities of interest through conference presentations and publications in a broad range of disciplinary and interdisciplinary venues. For example, the researchers on the team have at least 30 publications and participated in at least 28 conferences and presentations. A number of these publications and presentations are in top-tier conferences and journals with a wide audience range, including high performance computing, programming systems, computer vision, digital agriculture, plant phenotyping, data science, robotics, agricultural economics and livestock welfare, amongst others. Broad student and faculty representation across annual conferences was seen despite university imposed COVID-19 travel restrictions. See the "Products" and "Other Products" section for a list of all publications and presentations. A few members of the AIFARMS team gave prestigious keynote presentations or plenary talks at several research-focused conferences, including at the World Soils Congress, North American Plant Phenotyping Network (NAPPN) Annual Conference, the Artificial Intelligence, Machine Learning and Data Science World Forum 2022, and the International Workshop on Machine Learning on Graphs at the Conference on Web Search and Data Mining (2022). Engagement with Industry: Members of the AIFARMS team have had active discussions and meetings with personnel from several companies, including EarthSense, AGCO, Microsoft, Honeywell, John Deere, IBM, Verizon, T-Mobile, Corteva, and Indigo Ag. Most of these conversations are at an early exploratory stage, except EarthSense, which is a thriving and close collaboration, and AGCO which is further along in discussions. EarthSense is closely engaged with the autonomous farming and technology adoption thrusts. Adoption of two technology outcomes from the AIFARMS research, has begun. One outcome is the advances in learning for control, which were developed in thrust 1 to enable robust autonomous navigation in outdoor settings using computer-vision instead of (more expensive and yet less reliable) LIDAR+GPS. A second broad outcome is being developed in direct collaboration with EarthSense, in particular, autonomous under-canopy robots for planting cover crops before harvest, which leads to improved yields and significantly lower planting costs for the cover crops. This area of work builds on research on improved autonomy in thrust 1 and on ongoing research on the economics of cover crop adoption in thrust 5. Less than 5% of all croplands in the United States uses cover crops, despite their well-documented and substantial benefits, and increasing this adoption even by a small extent would be a significant win. In addition to the technology transfer discussions with companies, both Corteva and Microsoft have provided scholarships for students in the summer REU program this year. These companies were invited to mentor the students during the summer and to attend the end-of-summer REU project presentations. We aim to expand the pool of company scholarships in future years. Engagement with Government, Media, and Policy Research Organizations: Dr. Andrew Leakey, member of thrust 3, testified before the House Science, Space and Technology Subcommittee of the U.S. Congress at a hearing on "Bioenergy Research and Development for the Fuels and Chemicals of Tomorrow." This testimony included highlighting the synergy resulting from partnerships between the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) and AIFARMS. We have had several high-profile media outlets publish articles regarding the work in AIFARMS, including The Wall Street Journal, Wired, and Market place report on National Public Radio. Dr. Vikram Adve, PI of AIFARMS, has participated in a number of policy-focused events and meetings, including meeting with the Illinois state director of agriculture and his team; the Computing Community Consortium (CCC) Climate Roundtable's discussion group on digital agriculture; the Microsoft research faculty summit workshop on digital agriculture and the Future of Food; a panel discussion organized by the communications of the ACM on AI for Science; a AAAI Panel on the AI Institutes; a workshop on AI / Data Science for collaboration between land grant universities organized by the US National Academies of Sciences, Engineering and Medicine (NASEM) board on agriculture and natural resources; and a presentation to the FCC connectivity needs working group on future demands for rural broadband. Dr. Bruno Basso co-led a workshop titled, "Reducing the Health Impacts of the Nitrogen Problem: An Environmental Health Matters Workshop, Digital Agriculture to Reduce Nitrogen Losses across the U.S. Corn Belt," organized by the US National Academy of Sciences, Engineering and Medicine (NASEM). Dr. Jessica Wedow made a presentation on AIFARMS to the IoT4Ag Center - Seed of Innovation Seminar Series on July 27th, 2022. This seminar series brought together research centers focused on AI, digital, and precision agriculture to spark collaborations between the AI institutes and partnering academic institutions. We also reached the agricultural community more directly through participation at the Farm Progress show, interviews with the local national public radio station, the University of Illinois Agronomy Day demonstration and through specific focus groups with growers. Data Sharing and Management: The Data Management working group has created a set of slides for best practices in dataset creation accessible to the whole AIFARMS team. These slides are meant to be a quick reference point for guiding thrusts throughout the dataset building process starting with data collection stages, continuing with different levels of documentation, selection of the dataset and software license, to preservation and publication of the dataset. The AIFARMS Data Portal was launched in August 2022. The creation of this portal serves as a secure storage option for the AIFARMS team, allowing for long term storage, computational services, and between thrust and institution data sharing. The goal of this portal is to make a unified data storage portal for the team to increase collaboration at all levels. We believe this portal will be beneficial to multi-institutional projects that would benefit from shared computational and storage platforms. The Data Management team is working with individual researchers and thrusts to format, store and preserve the data that are in various stages of the preparation in a safe and secure manner, along with making many of them that are ready to be shared with a wider community openly available. Currently AIFARMS has published 4 open source datasets, with many more currently in the process of publication. For more examples, please reference "Products" and "Other Products". What do you plan to do during the next reporting period to accomplish the goals?AIFARMS has made significant progress on our goals in Year 2. We will continue to strive for more synergy between algorithmic, computational and practical agricultural technology within our thrusts. Continued cross-project collaborations were encouraged by our first in-person meeting since AIFARMS was formed, in March of 2022. We held break-out and group sections to discuss collaborations between subprojects and what institute leadership can do to foster these connections. Year 3 will expand upon these cross-thrust connections. Most subprojects will continue as planned into Year 3. Of note are some broad new directions, outlined below. A major new direction we will begin in Year 3 is an international collaboration with the PhenoRob Cluster of Excellence in Germany, funded by a supplemental award of $300,000 from USDA-NIFA. The goal of this effort is to lay the foundation for long-term collaborative research efforts between the PhenoRob Center and AIFARMS in areas of common interest. PhenoRob core projects are developing new technologies complementary to those in AIFARMS, and collaboration can bring solutions of significant value to US agricultural. Examples include 3D object reconstruction for robotic harvesting, chemical-free mechanical weeding, improved predictions from hyperspectral data, technology adoption studies complementary to those used in AIFARMS, and techniques for analyzing root performance. We will use the funding supplement to organize activities required for developing longer-term collaborations, including planning workshops; exchange visits; sharing of complementary data sets; proposal development; and joint publications. We will explore synergy between AIFARMS and three other large, projects at Illinois, newly funded by the USDA over the past 2 years: COALESCE (jointly funded by NSF and USDA-NIFA), DIRECT4AG and the Farm of the Future. A broad new effort we are continuing is to review our foundational AI research challenges in the light of the experience and initial results of the first two years of AI-for-agriculture research activities. For these seven foundational AI challenges, we are discussing how these challenges arise in specific AI-for-Agriculture problems, to develop a more detailed understanding of the research priorities we must tackle to make progress on the collaborative AI+agriculture research. A key goal of these discussions is to ensure that our research budget allocations are appropriately aligned to ensure progress on these research challenge areas. Rachel Switzky, Director of the Siebel Center for Design at UIUC, will be joining the team. In thrust 6, Switzky will expand the scope of the MEng degree in digital agriculture by adding a foundation in 'design thinking'. Her team will design a new course and review existing courses for the MEng degree. In thrust 1, Switzky will work with the members of the Human-centered Autonomy subproject to tackle key questions centered on how humans interact with advanced technologies, working with the natural language communication and knowledge applications for agricultural robots and the FarmBot. Thrust 3 will slightly reorganize their efforts to focus on three coordinated using a sorghum population. Over the last two years, AIFARMS Danforth site lead, Dr. Todd Mockler, has sequenced the genomes of over a third of a sorghum diversity panel. This panel is being phenotyped in Dr. Andrew Leakey's group at UIUC. These related data sets will enable the subprojects below to coordinate their efforts closely. We have shifted brought in two new AI faculty members, Dr. Yuxiong Wang and Dr. Mohammed El-Kebir, both from the UIUC Department of Computer Science, to enhance the focus of these projects. Dr. Wang is a specialist in open-world AI problems while Dr. El-Kebir is a specialist in AI techniques for genomics and bioinformatics. Sensor-to-phenotype predictions: We will continue what was done in Year 2 and bring in Dr. Yuxiong Wang to work with Dr. Andrew Leakey and Dr. Narendra Ahuja on this subproject. (Dr. Wang is also collaborating on thrust 1, where we are studying open-world computer vision tasks, such as object detection and segmentation.) Genome sequencing: This area has shifted directions and brought in the additional support of Dr. May Berenbaum, Department of Entomology and Plant Biology. The focus of this work will be on improved AI-driven techniques for genome sequence completion. Informed GWAS: This subproject has reorganized to focus on AI for GWAS, informed by the phenotype information of subproject 2 and the genome sequences of subproject 4, i.e., the outcomes of the above biological focus areas. This is an expansion of current efforts within the thrust with the addition of Dr. Mohammed El-Kebir. Dr. David Bullock, UIUC Department of Crop Science, will begin work on a new subproject with Dr. Hanghang Tong, an expert in heterogeneous data mining. This project will center around the Illinois Extension - Data-Intensive Farm Management project (DIFM) project, led by Bullock, to solve challenges with heterogeneous data sets. The DIFM project is structured as a loop, involving systems of sensors, real-time and just-in-time computational analysis, model-based information use, and upscaling made possible by technology that generates, processes, analyzes, ad acts upon digital information. Other general goals for Year 3 include the following: Continue active discussions with the companies that are ongoing in order to identify areas of mutual interest for joint collaborative projects. Previously, the AIFARMS team received sponsorships from two companies to support REU students we will expand this effort in Year 3. AIFARMS personal have meet with senior personnel at AGCO, during Year 3 we will continue these discussions and focus on collaborative areas. In Year 2, joint evaluation of autonomous planting of cover crops with Earthsense lead to 100 acres planted. The effort will grow to over 1000 acres in Year 3. Additional joint focus groups for farmers and development of an economic survey through a partnership with thrust 5. Several other preliminary discussions are under way with John Deere, ADM, EarthSense, and PepsiCo, as well as a few production livestock farms. Develop new connections with other national AI institutes, especially the other three funded by the USDA. Support public releases of AIFARMS data sets: The Data Management Working Group will continue to develop an AIFARMS data portal, which began last year, designed for both internal and external data sharing. They will work to facilitate making data sets and software tools available publicly through the data portal. Ramping up the capacity and usage of our experimental facilities, especially the Illinois Autonomous Farm and the new Illinois Farm of the Future: Much of the robotics, edge computing, and sensing research takes place or is tested on these facilities. The growth of these facilities in Year 3 will support an expanded range of synergistic projects, all of which will contribute to strengthening the outcomes and impact from AIFARMS. Explore collaborations between two institutes centered at UIUC, the NSF-funded Molecular Maker Laboratory Institute and DOE- funded Center for Advanced Bioenergy and Bioproducts Innovation. All three institutes share faculty and have complementary research goals. Currently, talks between AI researchers from both AIFARMS and MMLI around the broad topic of generative modeling are scheduled for early in Year 3. Additionally, CABBI and AIFARMS have plans to share research datasets interesting to both institutes and collaborate on several data management objectives. Expand the REU program to be able to support a larger cohort of students, including obtaining outside funding to reach more students, specifically from MSIs.

Impacts
What was accomplished under these goals? In Year 2 of operation, the AIFARMS Institute has continued to build on the strong foundation laid in Year 1 for a broad and impactful 5-year research program. The subprojects within the research thrusts bring together AI and Agriculture researchers in close collaborative efforts to explore how foundational advances in AI can impact important challenges in Agriculture. The list of subprojects has grown, with additional subprojects within the Soil Health and the Education and Outreach thrusts. The Education and Outreach thrust is contributing to important, impactful efforts for inspiring the younger generation to explore digital agriculture and to help grow a skilled digital agriculture workforce. A successful in-person REU program was hosted this summer, giving students hands-on research experience and career mentoring. Building on last year's successes with external research funding, several team members led a winning proposal to bring the USDA-NIFA Farm of the Future to the UIUC campus. Moreover, UIUC's role in hosting the AIFARMS Institute and our close partnership with Tuskegee University were both cited as key reasons for the win in an intensely competitive field. This project (I-FARM - Illinois Farming and Regenerative Management) will provide a valuable near-production-scale testbed to evaluate and demonstrate research outcomes from other major research efforts, including AIFARMS. Multiple new industry engagements launched this year or are currently under discussion with the potential to translate technical innovations from the group's research into commercial products. Our partnership with EarthSense is furthest along, with significant technology transfer successes in the areas of improving robot autonomy and cover crop planting. Team members have been invited to speak at many prominent research and education events and organized several community events. More specifically, some of the major accomplishments of the AIFARMS Institute in the second year are as follows. (Only the most important are included, due to space limitations; other accomplishments are listed in the complete report.) Details of the activities and outcomes that led to these accomplishments are described in Sections 2 and 3. Foundational AI Research We originally identified "foundational AI research challenges", which are common across many different domains, not specific to agriculture. In the second half of Year 2, we began a series of discussion meetings on these six research challenges (plus a 7th, Computer Vision). We also broadened the scope of the "Small Data Problem" to the more general class of "Open World AI". Advances on Open World AI (formerly called the Small Data Problem) Investigated the fundamental difference between convolutional neural networks that use complex-valued and real-valued parameters. There exists specifiable input to output mappings that complex-valued networks can perform but real-valued networks cannot. A novel transfer learning method based on Neural Network Gaussian Process to translate models from simulated to real-world data. Novel ML methods called heterophilic graph contrastive learning, which outperform previous supervised and SSL methods. Motivated by predicting soil N and P fluxes from soil datasets but with limited labeled data. Foundational advances in Computer Vision Novel framework for hierarchical (human) activity recognition from videos using deep neural networks New techniques for Heterogeneous Data Fusion (Also Foundational advances in Computer Vision) Significant progress on audio-visual input segmentation, by combining audio and vision data sources for improved segmentation results. Advances in Federated Edge Learning on Resource-constrained Devices Novel application-aware approximation tuning techniques to greatly reduce computational and energy requirements of neural networks while preserving end-to-end application quality goals. This was used to optimize vision-guided robot navigation and is now being explored for object manipulation (e.g., harvesting). Advances in Learning for Control CropFollow algorithm to enable vision-guided navigation without LiDAR or GPS for Level 2 autonomy. Strategies for Integrating Domain Knowledge into ML Efficient literature mining technique for entity linking in scientific literature is better than state-of-the-art methods and uses 60x fewer parameters. New techniques for Human Interaction with Autonomous Systems A new approach, Multimedia Generative Script Learning, to generate the next steps based on the goal and previous steps with visual scenes depicting the states of objects. Autonomous Farming Systems: This year, we demonstrated level 2 autonomy in field robots using both LIDAR + GPS and alternatively using (much cheaper and more robust) computer vision-guided models for navigation within crop rows. With the achievements in robot autonomy and foundational AI research, over 100 acres of cover-crops were planted well before harvest using autonomous under-canopy agricultural robots. Labor Optimization for Livestock Management: Audio monitoring can be combined with video to significantly improve individual tracking, behavior recognition, and other tasks for better livestock management. Thrust 2 researchers expanded on previously developed methods for separating mixed audio signal into constituent sources to predict the direction of audio sources by incorporating the scene geometry. Students from thrust 2 were able to apply these methods to the detection of COVID-19 infections with audio inputs, placing second in the worldwide DICOVA-II challenge. The thrust also developed three different pig and goat ethograms for behavior prediction with a variety of models. Environmental Resilience: The thrust created an image library containing >35,000 maize and sorghum leaf epidermal images, enabling the classification of common morphological features. Machine learning was used in combination with soil-canopy radiative transfer models for full use of proximal and airborne hyperspectral data for effective prediction of crop leaf and canopy level N status. In combination with the soil health thrust, these models were applied to predict SOC concentrations from proximal soil hyperspectral data. This result is in partnership with the soils thrust. Soil Monitoring and Health: In Year 2, the soils thrust was able to outfit two tile-drained fields in central Illinois with both wired and wireless sensor networks to measure input to output nutrient flux cycles. These fields led to the collection of 16,000 soil and plant measurements. With the rich dataset collected in Year 2, and existing datasets, novel methods based on graph neural networks were developed to predict soil N and P fluxes, which outperform previous supervised and semi-supervised learning methods. A new soil microbiome project launched and has begun with the collection of deep soil cores for in vitro microbiome analyses. Technology Adoption and Public Policy: In Year 2, thrust 5 extended their previous (static) weed ecology model with dynamic features to develop the Integrated Weed Economic-Ecology Dynamic (IWEED) model, to quantify yield loss caused by weeds depending on several static and dynamic input factors. Thrust members also established a choice experiment survey, sent to 10,000 US corn and soybean farmers, to examine factors driving willingness to plant cover crops with or without autonomous cover cropping robots. Education and Outreach: In Year 2, the education and outreach thrust has successfully hosted a K-12 computer science education summit, an REU summer intern program, CS-teacher endorsement training, and an AI foundry for agriculture applications summer workshop. In addition to these many programs, AIFARMS and the UIUC Center for Digital Agriculture received final Illinois state approval for a Master of Engineering in Digital Agriculture degree, expected to launch in Spring 2023.

Publications

  • Type: Journal Articles Status: Awaiting Publication Year Published: 2022 Citation: Cisneros-Velarde, P., Lyu, B., Koyejo, S. and Kolar, M. 2022. One Policy is Enough: Parallel Exploration with a Single Policy is Minimax Optimal for Reward-Free Reinforcement Learning, Arxiv, pre-print. https://arxiv.org/pdf/2205.15891.pdf.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Kamtikar, S., Marri, S., Walt, B., Uppalapati, K. N., Krishnan, G. and Chowdhary, G. 2022. Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment, IEEE Robotics and Automation Letters, 7(2). https://doi.org/5504-5511.10.1109/LRA.2022.3155821.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Khanna, M., Atallah, S. S., Kar, S., Sharma, B., Wu, L., Yu, C., Chowdhary, G., Soman, C. and Guan, K. 2022. Digital Transformation for a Sustainable Agriculture in the United States: Opportunities and Challenges. Agricultural Economics, 00, 1-14.?https://doi.org/10.1111/agec.12733.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Khanna, M. and Miao, R. 2022. Inducing the Adoption of Emerging Technologies for Sustainable Intensification of Food and Renewable Energy Production: Insights from Applied Economics. Australian Journal of Agricultural and Resource Economics, 66, 1-23. https://doi.org/10.1111/1467-8489.12461.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Lai, T., Ji,. H. and Zhai, C.X. 2022. Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking. Findings of the Association for Computational Linguistics, 36963711. https://doi.org/10.48550/arXiv.2202.13404.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Wang, S., Guan, K., Zhang, C., Lee, D., Margenot, A.J., Ge, Y., Peng, J., Zhou, W., Zhou, Q. and Huang, Y. 2022. Using Soil Library Hyperspectral Reflectance and Machine Learning to Predict Soil Organic Carbon: Assessing Potential of Airborne and Spaceborne Optical Soil Sensing. Remote Sensing of Environment, 271, 112914. https://doi.org/10.1016/j.rse.2022.112914.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Wu, J. and He, J. 2022. A Unified Meta-Learning Framework for Dynamic Transfer Learning. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 3573-3579. https://doi.org/10.24963/ijcai.2022/496.
  • Type: Journal Articles Status: Published Year Published: 2022 Citation: Bernard, G. C., Bolden-Tiller, O., Egnin, M., Bonsi, C., McKinstry, A., Landon, Z., Chen, Y. Y., Ritte, I., Archie, T., Shafait, M.D., Chowdhury, G., Charleston, C., Turner A., Brown, A., Idehen, O., Mitchell, I., Boone, J., Peterson C. and Lockett, A. 2022. The Use of Autonomous Robots to Address Labor Demands and Improve Efficacy in Agriculture, COJ Rob Artificial Intellgence, 1(5). https://crimsonpublishers.com/cojra/pdf/COJRA.000523.pdf.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Lai, T., Ji, H. and Zhai, C. 2021. BERT Might be Overkill: A Tiny But Effective Biomedical Entity Linker Based on Residual Convolutional Neural Networks. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 1631-1639. https://doi.org/10.18653/v1/2021.findings-emnlp.140.


Progress 09/01/20 to 08/31/21

Outputs
Target Audience:Research in AIFARMS has been disseminated to various target audiences;we have published several papers in peer reviewed journals and made presentations at professional meetings attended by other academic scientists. Through their engagement in AIFARMS research, undergraduate and graduate students as well as postdoctoral fellows are receiving training. AIFARMS team has also been reaching farmers, Extension agents and industry through podcasts, demonstrations and field days. We have also targeted the public as an audience for our activities through appearances on the local TV and at the Farm Progress Show. We are collaborating with several companies with an interest in our research to demonstrate and test practical applications and adapt our approaches to meet their needs. Changes/Problems:COVID-19 has strongly affected our capacity to meet in person. This inconvenience was quickly though partially overcome by regular online meetings within and between Thrusts, as well as between institutions and leadership teams. This problem also affected our ability to hold farmer surveys and focus groups that are important for the Technology Adoption research in Thrust 5, with only one small focus group possible in Year 1 and effectively delaying additional activities to Year 2. Also, as a result of COVID-19, students and staff could not travel together in one car. We mitigated this inconvenience by adding additional cars such that personnel could maintain social distancing. COVID-19 also prevented us from organizing our annual AIFARMS conference in the first year. Rather than organize a virtual meeting, we decided to cancel the event and hope to organize one in the middle or end of Year 2. The Advisory Board meeting and the NIFA and NSF funding agency reviews were originally intended to occur in person in conjunction with the annual conference, but were instead organized as virtual events via videoconference. COVID-19 has also affected the capacity to order and deliver instrumentation in times when needed (e.g. sensors in soil thrust). Alternative strategies were implemented by collecting data from other sources/type of sensors (e.g. drones collecting data on plant reflectance for nutrient content to compensate the lack of soil sensors). Another significant delay caused by the pandemic has been in hiring. The University of Illinois imposed a broad hiring freeze for many months in 2020, preventing or delaying the hiring of key staff members, including the Executive Director and the Assistant Director for Education and Outreach(both searches are well under way, and we expect them to complete soon). As for things to improve, we aim to have a stronger integration between Thrusts and between AI and crop modeling tools as data collection progresses. What opportunities for training and professional development has the project provided?In addition to the items illustrated in various Thrust Reports and the "Products" and "Other Products" sections, we would like to highlight the following opportunities provided by AIFARMS: Students and post-docs: Thirty-six (36) students and post-doctoral scientists work on AIFARMS projects. This includes nine (9) undergraduate students and five (5) post-doctoral scientists or visiting scholars. These students and post-docs are spread out across all of the AIFARMS thrusts and they are closely interactingin multidisciplinary fashion with AIFARM's soil scientists, plant scientists, animal scientists, computer scientists and sensor engineers. Post-docs and students are gaining valuable field experience (on farms or at animal facilities), and computer science students/post-docs have begun closely interacting with subject matter experts in soil science,animal science,genomics, andagronomy. Tenure-track faculty and senior researchers: Of the 42 senior researchers working at AIFARMS, 37 are tenure-track faculty or other senior researchers, spread out across the six thrusts. There is strong interaction among the tenure track faculty from disparate areas--soil science, animal science, computer sciences as joint projects have begun. This has enabled a new degree of necessary interdisciplinarity to help researchers involved in agriculture going forward. REU and undergraduate internships: In addition to post-graduate and graduate research, AIFARMS is also actively involved in the training of undergraduates. For instance, there were tenREU undergraduate interns embedded within AIFARMS projects. These tenstudents were all fully supported for two months during their summer internships, and participated in a number of AI+Agriculture research activities. They were also mentored in a wide range of career development goals, including the graduate school application process. Monthly seminars: Information is also disseminated via monthly seminars across AIFARMS and individual thrust meetings which are held weekly or once-in two weeks for each thrust. These thrust meetings are often attended by students and post-docs who gain valuable experience in the running of complicated, multi-faceted projects. Outreach: Outreach impacts training and professional development in both directions. This year there have been several outreach efforts with AIFARMS researchers participating in farmer focus groups, livestock farm visits, and related events. These interactions have helped the training of farmers to techniques developed by AIFARMS researchers and helped AIFARMS researchers' professional development from insights gained from non-academic customer environments. Joint research with industry:Research and serious technical interaction begun with Microsoft researchers, Windy City Labs engineers, and Terramar engineers has helped the professional development of AIFARMS members. Recruiting diverse students: We are working through the National Society of Minorities in Agriculture, Natural Resources and Related Sciences (MANRRS), to which TU and the University of Illinoishave strong affiliations, including the President-Elect who is a member of the AIFARMS leadership team. MANRRS will serve as a source of recruiting students for the project as well as provide social support and professional development opportunities for said participants. A subset of AIFARMS RA positions, with support from industry partners, will be dedicated to support those with Excluded Identities. iCAN program: This outreach program will address both the talent gap in tech and the underrepresentation of these groups in CS by providing foundational training in CS to agriculture students without said training at Illinois and partner institutions interested in professional development or obtaining a graduate degree in the Digital Agriculture program, thus bringing more students with agriculture backgrounds into computing. How have the results been disseminated to communities of interest?Results have been disseminated to academic communities of interest through conference presentations and publications in a broad range of disciplinary and cross-disciplinary venues. For example, the researchers on the team have published at least 14 papers in top-tier AI conferences, including NeurIPS, CVPR, ICCV, KDD and others. Research associated with Thrusts 3 and 4 was published in premier journals such as Nature Food and Nature Communications. These interdisciplinary journals have diverse audiences in agricultural sciences, engineering and computer sciences. Other research was published in leading economics journals. A few members of the AIFARMS team gave prestigious keynote presentations at conferences, including at the North American Plant Phenotyping Network (NAPPN) Annual Conference, and at SPLASH 2020, theACM SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity, a premier conference in programming languages. The AIFARMS team also made external presentations to diverse audiences interested in high performance computing, programming systems, computer vision, digital agriculture, plant phenotyping, data science, robotics, agricultural economics and livestock welfare, amongst others. See the Products section for a list of all publications and presentations. Dr. Bruno Basso (MSU) organized and chaired a workshop for the National Academies of Science, Engineering and Medicine on "Exploring a Dynamic Soil Information System." The aim of the workshop was to examine how soil resources nationally might be dynamically and accurately monitored towardseveral important goals for soil health. The high-level focus of the workshop reflects several of the science and technology priorities identified in the 2016 Framework for a Federal Strategic Plan for Soil Science. A report from this workshop is publicly available (see Products). Dr. Sanmi Koyejo organized and chaired a workshop funded by the NSF HDR TRIPODS program on "Asymptotics and Non-Asymptotics in Control and Reinforcement Learning." This foundational AI research topic is important and highly relevant to AIFARMS because Reinforcement learning (RL) is a highly active area of research, blending ideas and techniques from control, optimization, machine learning, and computer science. In fact, AIFARMS researchers are exploring RL techniques for problems as diverse as controlling autonomous mobile robots and extracting new spectral patterns from large, but underused hyperspectral (phenotyping) data sets. Dr. Alex Schwing co-organized a workshop at CVPR 2021 on "3D Scene Understanding for Vision, Graphics, and Robotics." This foundational AI research area is critical to multiple projects within AIFARMS, a few of which include reconstructing 3D plant structure from images despite extensive occlusion of internal details, recognizing animal behaviors and human-animal interactions in livestock farms, and recognizing the surroundings in autonomous robot navigation in fields. We have also built our internal community within AIFARMS with a monthly seminar series, allowing AIFARMS scientists to present their research to the whole team and to build cooperation during a year when travel was limited. Finally, we reached the agricultural community by participating at the Farm Progress show, the University of Illinois Agronomy Day demonstration and through specific focus groups with growers. For more examples please reference "Products" and "Other Products". What do you plan to do during the next reporting period to accomplish the goals?In this section, we focus on the broad plans for Year 2 spanning the overall project. AIFARMS has made significant progress in the first year, and we will continue to strive for even more synergy between algorithmic, computational and practical agricultural technology within our Thrusts. Individual academic groups in the different disciplines contained within each Thrust have already begun to interact synergistically in Year 1, despite the limitations imposed on launching a broad new Institute in the midst of the pandemic. As hopefully more interaction becomes possible, we anticipate that the number of productive interactions between teams will increase noticeably in the second year. We also expect our productivity on all goals to accelerate in Year 2 as the project becomes fully staffed. In particular, we anticipate that graduate students newly hired during Year 1 will start to become productive, and that more educational programs will come online as degrees become approved. In our core technology goals, for example the small data problem or computer vision, researchers tend to be extremely busy and new students take some time before they can make significant contributions. In many of the projects requiring physical agricultural research infrastructure, delays are inevitable before the facilities can be fully functional. The core education and infrastructure parts of the project will be leveraged to speed both student training and facility performance. Thus, outputs are expected to increase rapidly over Years 2 and 3. More specifically, the leadership team of AIFARMS has several broad goals for the second year: Providing streamlined and regular scientific meetings and other interaction opportunities: A new monthly Distinguished Lecture Series, co-organized with the Center for Digital Agriculture, will bring in external speakers in a wide range of topics related to AI in digital agriculture. We will continue our regular, internal monthly all-hands team meeting where AIFARMS members present their research. Driving increased interactions between subprojects and even across thrusts: One example is to leverage the FedSSL software framework being developed in subproject 4 of Thrust 1 for more easily training machine learning models in berry picking (subproject 2 of Thrust 1), stomatal conductance in Thrust 3, and video monitoring of pigs in Thrust 4. Another example is expanding the interaction between Thrusts 3 and 4 on soil carbon measurement and prediction. Matchmaking between scientists with complementary expertise to solve specific problems. As one example, we will bring online one or two more subprojects to address newly-identified priorities in agricultural AI research. Encourage more subprojects within the team to make data, software and tools available publicly: Several subprojects are developing data sets (e.g., soils, pig videos) and software tools (e.g., FedSSL for semi-supervised learning, and the AVAT or Animal Video Analysis Tool for annotating videos of livestock), which could hold great value for the broader research community. The leadership, through the Data Management Working Group, will work to facilitate making these data sets and software tools available publicly, likely through the AIFARMS Github account. Ramping up the capacity and usage of our experimental facilities, especially the Illinois Autonomous Farm: Much of the robotics, edge computing, and sensing research takes place on this facility. The growth of IAF in Year 1, together with the new funding sources obtained in the first year, will support an expanded range of synergistic projects, all of which will contribute to strengthening the outcomes and impact from AIFARMS. Obtaining funding to expand and grow the REU program: We aim to support more students in Summer 2022, and hopefully also accommodate students from other BIPOC-serving institutions. All students in the program were fully supported last summer, and the leadership team will pursue funding opportunities to enable such an expansion while supporting all the students next year as well. Organizing in-person meetings and conferences: We anticipate that in-person events will become possible in Year 2, enabling us to organize such events. We are particularly keen to organize our in-person annual conference, after having to cancel that event for Year 1. Such events will enhance the educational experiences of our students as well as the quality of scientific communication within the team. Exploring synergy in the next year between AIFARMS and other large, newly-funded projects at Illinois over the past year: The NSF Center for Research On Programmable Plant Systems (CROPPS) may benefit from the AI, Machine Learning, Autonomous Farming, and Genomics research happening in AIFARMS, and conversely, AIFARMS may benefit from the new directions in plant breeding, programmable plants as sensors, and "Internet of Living Things" (IoLT) goals of CROPPS. The IBM-Illinois Discovery Accelerator Institute addresses a number of research areas with specific overlapping goals, including edge computing, artificial intelligence, carbon accounting, and carbon sequestration.

Impacts
What was accomplished under these goals? In its first year of operation, the AIFARMS Institute has laid a strong foundation for a broad and impactful five-year research program. The Strategic and Implementation Plan describes the vision, the key research, education and outreach thrusts, the organizational structure, and other important planning activities that are key to laying such a foundation. The twentysubprojects within fiveresearch thrusts all bring together AI and agriculture researchers in close collaborative efforts to explore how foundational advances in AI can impact important challenges in agriculture. The Education and Outreach thrust is contributing to important, impactful efforts for inspiring the nextgeneration to explore digital agriculture and to help grow a skilled digital agriculture workforce. A successful in-person REU program gave a number of students hands-on research experience as well as wide-ranging career mentoring. New external research funding sources obtained by team members for closely related and complementary research significantly expands our scope and potential for impact. Multiple new industry engagements launched this year or currently under discussion have the potential to translate technical innovations from the group's research into commercial products. Team members have been invited to speak at many prominent research and education events, and organized several valuable community events. More specifically, the major accomplishments of the AIFARMS Institute in the first year are as follows (a few of the most significant accomplishments are marked with §). Accomplishments in Foundational AI Research We have identified six "foundational AI research challenges," which arise repeatedly in many different domains, including digital agriculture, and each of these impacts several different subprojects within AIFARMS. These foundational challenges are described briefly in the Strategic Plan. During the first year, we have made significant progress on all these six problems. We have also tackled some fundamental challenges in Computer Vision, which is a critical technology in digital agriculture. Our accomplishments in these foundational areas are as follows. Advances on the Small Data Problem: We advanced algorithms for rapid adaptation of neural networks for control with few data points (Havens and Chowdhary, 2021). We also developed datasets and problem formulations for semi-supervised learning for identification of berries and obtained preliminary results. §We developed techniques based on continuous transfer learning, in which we take as input both a static source domain with abundant labeled data, as well as a time-evolving unlabeled target domain, and output a predictive model for the target domain at the current time stamp. Our proposed method is based on a novel label-informed domain discrepancy measure, and it effectively integrates a variational autoencoder for feature extraction. §In another strategy based on "indirect invisible poisoning attacks," we take as input a base algorithm, labeled source data, as well as unlabeled target data, and output poisoned data for the source domain that degrades the predictive performance on the target domain. Our proposed method is based on bi-level optimization, where the objective function uses the label-informed domain discrepancy in the source domain. Foundational Advances in Computer Vision: Novel algorithms for understanding object movement in video data using multi-object tracking and segmentation (MOTS). §Fundamental activity recognition technology in computer vision, developed so far using human dance but also applicable to livestock. Models human activity as a hierarchical process which is consistent with how experts view it. Our model spans low levels (raw images, image sequences), to mid-levels (human poses and body part movements), to high levels (sequences of actions that form semantic units) of human activity. We expect to extend this capability to the case of pigs, instead of humans. New Techniques for Heterogeneous Data Fusion : Two of the techniques above for the "small data" problem - continuous transfer learning, and indirect invisible poisoning attacks - also provide novel benefits for learning from heterogeneous data sources. §Another new approach to heterogeneous data fusion uses a novel model called Network of Tensor Time Series (NeT3), which includes separate models to incorporate explicit relationship networks of the time series and model the implicit relationships among co-evolving time series. Advances in Learning for Control: §We have significantly improved the reliability of small robot navigation amongst corn and soybean fields using visual control learning, replacing expensive LIDAR and GPS sensors with far less expensive consumer video cameras, supported by recent advances in Convolutional Neural Networks (CNNs) for distance and heading (angle) estimation from a live video stream. (Also contributes to the foundational challenge of edge computing for machine learning.) We demonstrated through extensive field experiments that recent advances in CNN model pruning can dramatically reduce the computational requirements for mobile robot navigation without sacrificing navigation reliability. These optimizations serve to bring down the performance, energy requirements, and even the cost of computer hardware for robots by an order of magnitude or more. Strategies for Integrating Domain Knowledge into ML: Work on deep learning for control showed that it is possible to create hybrid reinforcement learning controllers that use approximate models of the system to guarantee stability (Narenthiran et al, 2021) In activity recognition, we use prior knowledge about constraints on parameters of motion that apply to humans to improve the accuracy of the generic models used for the predictions. Further, we expect to generalize the capability to separate the domain-dependent and domain-independent components. New techniques for Human Interaction with Autonomous Systems : §We developed natural language learning algorithms that learned keywords from WikiHow webpages for entity discovery and schema induction for procedural knowledge (Wang et al, 2021), to help answer gardening questions.

Publications

  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: 1. National Academies of Sciences, Engineering, and Medicine. Exploring a Dynamic Soil Information System: Proceedings of a Workshop. Washington, DC: The National Academies Press, 2021. https://doi.org/10.17226/26170.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 2. Basso, B. Precision conservation for a changing climate. Nat Food (2021). https://doi.org/10.1038/s43016-021-00283-z
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 3. Basso, B., Martinez-Feria, R.A., Rill, L. et al. Contrasting long-term temperature trends reveal minor changes in projected potential evapotranspiration in the US Midwest. Nat Commun 12, 1476 (2021). https://doi.org/10.1038/s41467-021-21763-7
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: 4. Khanna, M. Digital Transformation of the Agricultural Sector: Pathways, Drivers and Policy Implications Applied Economic Perspectives and Policy, October, 2020; https://doi.org/10.1002/aepp.13103
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 5. Khanna, M and R. Miao, Inducing the Adoption of Emerging Technologies for Sustainable Intensification of Food and Renewable Energy Production: Insights from Applied Economics Australian Journal of Agricultural and Resource Economics (revise and resubmit)
  • Type: Journal Articles Status: Under Review Year Published: 2021 Citation: 6. Khanna, M., S. Atallah, S. Kar, B. Sharma, L. Wu, C. Yu, G. Chowdhary and C. Soman, Digital Transformation for a Sustainable Agriculture in the US: Opportunities and Challenges under preparation for submission to Agricultural Economics
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 7. A.Shirke,A. Saifuddin, A.Green-Miller, I. Condotta, A. Kotnana,O. Kocabalkanli, N.Ahuja, R. N.Dilger, and M. Caesar. "Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras". AgEng2021.
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2021 Citation: 8. A. Shirke, J. Li, A. Green-Miller, T. Williams, X. Hu, A. Luthra, N. Ahuja, M. Caesar. "Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras".CV4Animals CVPR Workshop 2021. Accepted.
  • Type: Journal Articles Status: Submitted Year Published: 2021 Citation: 9. Active Learning with Graph Neural Network Dynamics, submitted to NeurIPS 2021, in collaboration with Andrew, Supratik
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 10. J. Wu, J. He. Indirect Invisible Poisoning Attacks on Domain Adaptation. KDD 2021
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 11. B. Jing, H. Tong, Y. Zhu. Network of Tensor Time Series. WWW 2021
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: 12. I.-J. Liu, R. Yeh, A.G. Schwing. High-Throughput Synchronous RL. NeurIPS 2020
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: 13. Z. Ren, R. Yeh, A.G. Schwing. Not all unlabeled data are equal: Learning to weight data in semi-supervised learning. NeurIPS 2020
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: 14. R. Sun, T. Fang, A.G. Schwing. Towards a better global loss landscape of GANs. NeurIPS 2020
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 15. P. Zhuang, O. Koyejo, A.G. Schwing. Enjoy your Editing: Controllable GANs for Image Editing via Latent Space Navigation. ICLR 2021
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 16. Z. Ren, I. Misra, A.G. Schwing, R. Girdhar. 3D Spatial Recognition without Spatially Labeled 3d. CVPR 2021
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 17. C. Graber, G. Tsai, M. Firman, G. Brostow, A.G. Schwing. Panoptic Segmentation Forecasting. CVPR 2021
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 18. Y.-T. Hu, J. Wang, R. Yeh, A.G. Schwing. SAIL-VOS 3D: A synthetic dataset and baselines for object detection and 3D mesh reconstruction from video data. CVPR 2021
  • Type: Journal Articles Status: Under Review Year Published: 2021 Citation: 19. Wang, S., Guan, K., Ainsworth, E.A. et al. Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield by machine learning and radiative transfer modeling. International Journal of Applied Earth Observation and Geoinformation
  • Type: Journal Articles Status: Under Review Year Published: 2021 Citation: 20. Zhou, Q., Wang, S., Guan, K. et al., High-performance atmospheric correction of airborne hyperspectral imaging spectroscopy: model intercomparison, parameter retrieval, and machine learning surrogates. Remote Sensing of Environment
  • Type: Journal Articles Status: Under Review Year Published: 2021 Citation: 21. Wang, S., Guan, K., et al. Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: assessing potential of airborne and spaceborne optical soil sensing. Remote Sensing of Environment.
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 22. Baoyu Jing, Hanghang Tong, Yada Zhu: Network of Tensor Time Series. WWW 2021: 2425-2437
  • Type: Other Status: Under Review Year Published: 2021 Citation: 23. Baoyu Jing, Si Zhang, Yada Zhu, Bin Peng, Kaiyu Guan, Hanghang Tong: iTime: Instance Guided Time Series Imputation
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2021 Citation: 24. N. Ahuja and N. Mahajan, ICCV 2021: Compact, Explainable Deep Learning models
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2021 Citation: 25. Williams, T. and A. Green-Miller. (2021). Engineered Resilience in Livestock for Improved Animal Welfare. Abstract Accepted for ASAS Conference (towards Journal of Animal Science)
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2021 Citation: 26. X. Hu and N. Ahuja, ICCV 2021, HumanPose Sequence Estimation and Recognition
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2021 Citation: 27. M. Chatterjee, A. Cherian, N. Ahuja, ICCV 2021: Audio-Visual Fusion
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: 28. Basso, B. & Antle, J. Nature Sustainability. 3, 254256 (2020)
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 29. Basso B (2021) Precision conservation for a changing climate. Nature Food 2: 322-323
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 30. Basso B, et al., (2021) Contrasting long-term temperature trends reveal minor changes in projected potential evapotranspiration in the US Midwest. Nature Communications 12: 1476
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 31. Maestrini B, Basso B (2021) Subfield crop yields and temporal stability in thousands of US Midwest fields. Precision Agriculture DOI: 10.1007/s11119-021-09810-1.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: 32. Northup, D, Basso. B et al., 2021. Novel Technologies for Emission Reduction Complement Conservation Agriculture To Achieve Negative Emissions From Row Crop Production. Proceedings National Academy of Sciences, Vol. 118 No. 28
  • Type: Journal Articles Status: Published Year Published: 2021 Citation: 33. Jiayang Xie, Samuel B Fernandes, Dustin Mayfield-Jones, Gorka Erice, Min Choi, Alexander E Lipka, Andrew D B Leakey, Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping, Plant Physiology, 2021;, kiab299, https://doi.org/10.1093/plphys/kiab299
  • Type: Conference Papers and Presentations Status: Other Year Published: 2021 Citation: 1. Khanna, M. Digital Transformation for a Sustainable Agriculture in the US: Opportunities and Challenges International Agricultural Economics Association Conference, August 29, 2021.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2021 Citation: 2. Khanna, M. Economic Incentives for Robotic Weed Control Cluster of Excellence PhenoRob  Robotics and Phenotyping for Sustainable Crop Production at the University of Bonn, Sept.24, 2021.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2021 Citation: 3. Finger, R., Huber, R., Wang, Y and M. Khanna, Panel Discussion: Digital innovations for more sustainable agricultural landscapes, Landscape 2021, Berlin, September 20-22, 2021.
  • Type: Conference Papers and Presentations Status: Other Year Published: 2021 Citation: 4. Elizabeth Ainsworth, Using hyperspectral reflectance to estimate and map photosynthesis in a soybean NAM population. Plenary Talk, North American Plant Phenotyping Network Annual Conference, February 2021 (online).
  • Type: Conference Papers and Presentations Status: Other Year Published: 2021 Citation: 5. Tiffani Williams, The Art and Science of Black Farming, STEM Illinois Communiversity Think & Do Tank and Carle Illinois College of Medicine, February 27, 2021.
  • Type: Other Status: Other Year Published: 2021 Citation: 6. Bruno Basso, US National Academy of Sciences, Engineering and Medicine, Reducing the Health Impacts of the Nitrogen Problem: An Environmental Health Matters Workshop, Digital Agriculture to Reduce Nitrogen Losses across the U.S. Corn Belt. Virtual meeting
  • Type: Other Status: Other Year Published: 2021 Citation: 7. Bruno Basso, 2021 Columbia University, Integrating crop models, AI, and sensing for scaling sustainable agricultural systems
  • Type: Conference Papers and Presentations Status: Other Year Published: 2021 Citation: 8. Bruno Basso, 2021 AgMIP annual meeting, Modeling Circular Agricultural Systems, Columbia University.
  • Type: Other Status: Other Year Published: 2021 Citation: 9. Andrew Leakey, The Phenomics of Stomata and Water Use Efficiency in C4 crops (December 2020). ARPA-E TERRA Program PIs Meeting
  • Type: Other Status: Other Year Published: 2021 Citation: 10. Andrew Leakey, The Phenomics of Stomata and Water Use Efficiency in C4 crops (October 2020). Martin and Ruth Massengale Lecture to the Annual Meeting of the Crop Science Society of America
  • Type: Other Status: Other Year Published: 2021 Citation: 11. Andrew Leakey, The Phenomics of Stomata and Water Use Efficiency in C4 crops (Feb 2021). University of Missouri Interdisciplinary Plant Group seminar
  • Type: Other Status: Other Year Published: 2021 Citation: 12. Andrew Leakey, The Phenomics of Stomata and Water Use Efficiency in C4 crops (March 2021). UIUC Department of Plant Biology colloquium
  • Type: Other Status: Other Year Published: 2021 Citation: 13. Andrew Leakey, The Phenomics of Stomata and Water Use Efficiency in C4 crops (April 2021). DOE BRC Sorghum workshop
  • Type: Other Status: Other Year Published: 2021 Citation: 14. Andrew Leakey, Overcoming bottlenecks in field-based root phenotyping using thousands of minirhizotrons (May 2021). 11th Symposium of the International Society of Root Research and Rooting 2021
  • Type: Other Status: Other Year Published: 2021 Citation: 15. Andrew Leakey, Phenotyping stomatal anatomy and function (Sept 2021) Society for Experimental Biology Environmental Physiology Group, Virtual Workshop on Field and Laboratory Techniques.
  • Type: Other Status: Other Year Published: 2021 Citation: 16. Alex Schwing, AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability, Vision for Agriculture Workshop at CVPR, 2021.
  • Type: Other Status: Other Year Published: 2020 Citation: 17. Vikram Adve, AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability, Washington State University Digital Agriculture Summit: How AI & Cyberinfrastructure are Impacting the Evolution of Digital Agriculture, Oct. 7, 2020.
  • Type: Other Status: Other Year Published: 2020 Citation: 18. Vikram Adve, AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability, SRC Center for Applications Driving Architectures (ADA) Fall Symposium, Nov. 10, 2020.
  • Type: Other Status: Other Year Published: 2020 Citation: 19. Vikram Adve, Why Digital Agriculture is Fertile Ground for Software Systems Research, SPLASH 2020, the ACM SIGPLAN conference on Systems, Programming, Languages, and Applications: Software for Humanity (Keynote Presentation), Nov 19, 2020
  • Type: Other Status: Other Year Published: 2021 Citation: 20. Vikram Adve, AI for Agricultural Innovation, Grainger College of Engineering AI Research Webinar, Feb. 12, 2021
  • Type: Other Status: Other Year Published: 2021 Citation: 21. Vikram Adve, AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability, Virginia Tech Center for Advanced Innovation in Agriculture (CAIA), March 23, 2021
  • Type: Conference Papers and Presentations Status: Accepted Year Published: 2021 Citation: 22. Vikram Adve, Computational Needs for the AIFARMS National AI Institute, Coalition for Academic Scientific Computation (CASC) Annual Spring Conference, April 7, 2021
  • Type: Other Status: Other Year Published: 2021 Citation: 23. Vikram Adve, AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability,, Digital/AI Seminar Series of the Centre for Bhutan Studies and GNH, June 3, 2021.