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, 2022
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 09/01/20 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: 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: 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: 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.