AIFARMS

PUBLICATIONS AND PRESENTATIONS

Publications:
  • Adve, V. S., Wedow, J. M., Ainsworth, E. A., Chowdhary, G., Green-Miller, A., Tucker, C., (2024). AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability. AI Magazine. https://doi.org/10.1002/aaai.12152
  • Wang, S., Guan K., Zhang, C., Jiang, C., Zhou, Q., Li, K., Qin, Z., Ainsworth, E.A., He, J., Wu, J., Schaefer, D., Gentry, L., Margenot, A., Herzberger, L., (2023). Airborne hyperspectral imaging of cover crop growth through radiative transfer process-guided machine learning. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2022.113386
  • Wang, Q., Li, M., Chan, H. P., Huang, L., Hockenmaier, J., Chowdhary, G., & Ji, H. (2022). Multimedia Generative Script Learning for Task Planning. Findings of ACL 2023 https://doi.org/10.48550/arXiv.2208.12306
Presentations:
  • Bushman, J., Condotta, I, Knox, R., Caesar, M., Green-Miller, A. (May, 2023). I-SEEDS: Illinois System for Electronic Estrus Detection and Stimulation. [Poster] Proceedings of 2nd US-PLF Conference.
  • Condotta, I, Benicio, L., Dunning, N., Dilger, R. (May, 2023). I-PICS: Illinois Pig Identification through Computer vision System. In: [Presentation]. Proceedings of 2nd US-PLF Conference.
  • Khanna. M. (February 2023). Owning robots or hiring services for weed management? Bioeconomic and behavioral drivers. [Conference presentation]. The Eastern Economic Association 49th Annual Conference.
  •  Khanna. M. (April 2023). Owning robots or hiring services for weed management? Bioeconomic and behavioral drivers. [Conference presentation]. The Midwest Economics Association 87th Annual Meetings.

2022

Publications:
  • 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., Lockett, A. (2022). The Use of Autonomous Robots to Address Labor Demands and Improve Efficacy in Agriculture, COJ Rob Artificial Intelligence, 1(5). https://crimsonpublishers.com/cojra/pdf/COJRA.000523.pdf
  • Cisneros-Velarde, P., Lyu, B., Koyejo, S., 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
  • Gasparino, M. V., Sivakumar, A. N., Liu, Y., Velasquez, A. E., Higuti, V. A., Rogers, J., … & Chowdhary, G. (2022). Wayfast: Navigation with predictive traversability in the field. IEEE Robotics and Automation Letters, 7(4), 10651-10658. https://doi.org/10.1109/LRA.2022.3193464
  • Gasparino, M. V., A. E., Higuti, Sivakumar, A. N., Velasquez, A. E. B., Becker, M., & Chowdhary, G. (2023). CropNav: A Framework for Autonomous Navigation in Real Farms. Under review at IEEE International Conference on Robotics and Automation.
  • Jing, B., Feng, S., Xiang, Y., Chen, X., Chen, Y., & Tong, H. (2021). X-GOAL: Multiplex Heterogeneous Graph Prototypical Contrastive Learning. Proceedings of the 31st ACM International Conference on Information & Knowledge Management. (CIKM’2022), 894-904. arXiv. https://doi.org/10.1145/3511808.3557490.
  • Jing, B., Yan, Y., Zhu, Y., & Tong, H. (2022). COIN: Co-Cluster Infomax for Bipartite Graphs. NeurlPS 2022 GLFrontier Workshop. arXiv. https://doi.org/10.48550/arXiv.2206.00006
  • Jing, B., Zhang, S., Zhu, Y., Peng, B., Guan, K., Margenot, A., & Tong, H. (2022). Retrieval Based Time Series Forecasting. 31st ACM International Conference on Information & Knowledge Management. (CIKM’2022), AMLTS Workshop.  arXiv. https://doi.org/10.48550/arXiv.2209.13525
  • Ji, T., Dong, R., & Driggs-Campbell, K. (2022). Traversing Supervisor Problem: An Approximately Optimal Approach to Multi-Robot Assistance. Robotics: Science and Systems (RSS). https://doi.org/10.48550/arXiv.2205.01768.
  • Kamboj, A., Ji, T., & Driggs-Campbell. K. (2022).  Examining Audio Communication Mechanisms for Supervising Fleets of Agricultural Robots. IEEE International Conference on Robot & Human Interactive Communication (RO-MAN).
  • Kamtikar, S., Marri, S., Walt, B., Uppalapati, K. N., Krishnan, G., 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
  • Khanna, M., Atallah, S. S., Kar, S., Sharma, B., Wu, L., Yu, C., Chowdhary, G., Soman, C., 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
  • Khanna, M., 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
  • Lai, T., Ji,. H., Zhai, C.X. (2022). Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking.  Findings of the Association for Computational Linguistics, 3696–3711. https://doi.org/10.48550/arXiv.2202.13404
  • Li, J., Green-Miller, A. R., Hu, X., Lucic, A., Mahesh Mohan, M., Dilger, R. N., Condotta, I. C., Aldridge, B., Hart, J. M., & Ahuja, N. (2022). Barriers to computer vision applications in pig production facilities. Computers and Electronics in Agriculture200, 107227. https://doi.org/10.1016/j.compag.2022.107227
  • Li, N., Zhou, S., Margenot, A.J. 2023. From prairie to crop: spatiotemporal dynamics of surface soil organic carbon stocks over 167 years in Illinois, U.S.A. Science of the Total Environment. 159038. https://doi.org/10.1016/j.scitotenv.2022.159038 
  • Sivakumar, A. N., Modi, S., Gasparino, M. V., Ellis, C., Velasquez, A. E. B., Chowdhary, G., & Gupta, S. (2021). Learned visual navigation for under-canopy agricultural robots. In Proceedings of Robotics: Science and Systems, Virtual, July 2021. https://doi.org/10.48550/arXiv.2107.02792
  • Wang, Q., Li, M., Chan, H. P., Huang, L., Hockenmaier, J., Chowdhary, G., & Ji, H. (2022). Multimedia Generative Script Learning for Task Planning. arXiv. https://doi.org/10.48550/arXiv.2208.12306. (Submitted to ICLR 2023)
  • 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
  • Wang, H., Huang, W., Wu, Z., Tong, H., Margenot, A.J., He, J. 2022. Deep Active Learning by Leveraging Training Dynamics. NeurIPS. Accepted.
  • Wei, T., You, Y., Chen, T., Shen, Y., He, J., & Wang, Z. (2022). Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative. NeurlPS 2022. arXiv. https://doi.org/10.48550/arXiv.2210.03801
  • Wu, J., 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
  • Wu, J., He, J., Wang, S., Guan, K., & Ainsworth, E.A. (2022). Distribution-Informed Neural Networks for Domain Adaptation Regression. NeurIPS 2022.
  • Wu, J., He, J., & Ainsworth, E. (2022). Non-IID Transfer Learning on Graphs. Accepted by Conference on Artificial Intelligence (AAAI-23). arXiv. https://doi.org/10.48550/arXiv.2212.08174
  • Yan, Y., Zhou, Q., Li, J., Abdelzaher, T., Tong. H. (2022). Dissecting Cross-Layer Dependency Inference on Multi-Layered Inter-Dependent Networks, Proceedings of the 31st ACM International Conference on Information & Knowledge Management. (CIKM’2022), 2341-2351. https://doi.org/10.1145/3511808.3557291
  • Zhang, Z., Du, B., & Tong, H. (2022). SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation. Proceedings of the 31st ACM International Conference on Information & Knowledge Management. (CIKM’2022), 4712-4716. arXiv. https://doi.org/10.48550/arXiv.2205.11231.
  • Zhou, Y., Wu, J., Wang, H., & He, J. (2020). Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning. 31st ACM International Conference on Information & Knowledge Management. (CIKM’2022), arXiv. https://doi.org/10.48550/arXiv.2009.09026
  • Zhou, Q., Wang, S., Liu, N., Townsend, P., Jiang, C., Peng, B., Verhoef, W., Guan, K., High-performance atmospheric correction of airborne hyperspectral imaging spectroscopy: model intercomparison, key parameter analysis, and machine learning surrogates. ISPRS Journal of Photogrammetry and Remote Sensing. (Accepted).
Presentations:
  • Adve, V. (2022, February). National AI Institutes program panel – Overview of AIFARMS [Panel presentation]. AAAI Conference on Artificial Intelligence. Online.
  • Adve, V. (2022, February). An overview of AIFARMS [Presentation]. Agricultural Genome to Phenome Initiative Field Day, Online. https://www.ag2pi.org/workshops-and-activities/field-day-2022-02-16/
  • Adve, V. (2022). A perspective on AI for Science [Panel discussion]. Communications of the ACM, Online.
  • Becker, M (October 2022). Presented to the Ag teaching staff at the Chicago High School for Agricultural Sciences.
  • Becker, M (November 2022). Presented to the Board of Directors for the Elevate 217 non-profit in Mattoon, IL.
  • Bernard, G.C. (2022). Current research and new agricultural technologies [Presentation]. Center for Research Excellence Symposium Research, Alcorn State University, Lorman, MS, United States.
  • Bernard, G.C. (2022). Current research developments, modern farming technologies, including autonomous farming and career readiness [Presentation]. North Carolina A&T State University, Greensboro, NC, United States.
  • Bolden-Tiller, O. (2022, February). Tuskegee University Project Updates/ Opportunities [Conference presentation]. 130th Annual Small Farmers Conference, Tuskegee, AL, United States.
  • Bolden-Tiller, O., Bernard, G. C., Adve, V. S. (July, 2022). Can data science and Artificial Intelligence (AI) enable new collaborative platforms between diverse land-grant institutions and create more impactful outcomes? [Meeting, with presentations]. National Academies Sciences, Engineering, and Medicine. Online. https://www.nationalacademies.org/event/07-27-2022/enhancing-collaboration-and-deepening-impact-can-data-science-and-artificial-intelligence-ai-enable-new-collaborative-platforms-between-diverse-land-grant-institutions-and-create-more-impactful-outcomes
  • Guha, S. (2022, April). IoT Sensor networks for soil and water quality [Presentation]. Environmental Engineering Department, University of Wisconsin-Madison, Madison, WI, United States.
  • He, J. (2022, February). Towards Understanding Rare Categories on Graphs [Conference presentation]. The International Workshop on Machine Learning on Graphs.
  • Khanna, M. (2022, March). Economic Incentives for Robotic Weed Control in Row Crop Agriculture [Conference presentation]. DigiCrop 200 PhenoRob, Online. https://www.youtube.com/watch?v=PiTwrLT_JcE
  • Khanna. M. (August 2022). Bioeconomic and behavioral incentives for robotic weed control in row crop agriculture [Conference presentation]. Agricultural and Applied Economics Association (AAEA) Annual Meeting.
  •  Khanna. M. (November 2022). Bioeconomic and behavioral incentives for robotic weed control in row crop agriculture [Conference presentation]. The Southern Economic Association 92nd Annual Meeting.
  • Kamtikar, S. (2022, October). Realistic Simulation Environments to Achieve Visual Servoing on Soft Continuum Arms in Constrained Environments.[Presentation]. Fourth International Workshop on Machine Learning for Cyber-Agricultural Systems (MLCAS 2022).
  • Kuhl, A.S., Guha, S., Matamala, R., He, J., Tong, H., Wei, T., Douglass, M., Kemner, J., and A.J. Margenot., (December 2022). An AI Approach to Understanding Soil Health and Nutrient Cycling. American Geophysical Union Fall Meeting.
  • Leakey, A. (2022, March). Bioenergy Research and Development for the Fuels and Chemicals of Tomorrow. Energy Subcommittee [Congressional House Committee]. Energy subcommittee hearing. Online. https://republicans-science.house.gov/hearings?ID=2E8CE7A5-616B-4794-A3EA-C88BE42D35BEHearing – Bioenergy Research and Development for the Fuels and Chemicals of Tomorrow – Hearings – House Committee on Science Space & Tech – Republicans
  • Leakey, A. (May 2022). The Phenomics of Stomata and Water Use Efficiency in C4 crops [Keynote]. UIUC Institute for Genomics Biology, Fellow’s Symposium.
  • Leakey, A. (May 2022). Phenomics of Stomata and Water Use Efficiency in C4 crops. [Presentation]. Center for Sorghum Improvement Seminar, Kansas State University.
  • Leakey, A. (June 2022). Phenomics of Stomata and Water Use Efficiency in C4 crops. [Presentation]. Gordon Research Conference, Vascular Plant Biology.
  • Leakey, A. (December 2022). Interdisciplinary research in the Center for Advanced Bioenergy and Bioproducts Innovation. [Presentation]. Oklahoma State University, Biobased Products and Energy Center.
  • Li, N., Margenot, A.J. (November 2022). Distinct Soil Health Indicators Are Associated with on-Farm Variation in Maize Yield and Tile Drain Nitrate Losses across Contrasting Nitrogen Application in Central Illinois. [Conference presentation]. ASA-CSSA-SSSA Annual Meeting. Baltimore, MD.
  • Li, N., Margenot, A.J. (November 2022). Quantifying Field-Scale SOC Stock: Fusion Remote and Proximal Sensing Data [Conference presentation]. ASA-CSSA-SSSA Annual Meeting. Baltimore, MD. Nov 6-8, 2022.
  • Tong. H. (2022, February). NetFair: Toward the Why Question of Network Mining’ at Artificial Intelligence [Conference presentation]. Machine Learning and Data Science World Forum, Online.
  • Tong, H. (2022). User Response Prediction and Beyond: A Graph-based Approach. [Keynote]. Meta.
  • Tucker, C. (November 2022). CDA/AIFARMS Overview and Educational Activities. UIUC Ag Tech Breakfast.
  • Tucker, C. (2022, February). History of Agriculture and Innovations [Education outreach presentation].National 4H Agriculture Innovators Challenge Training, Urbana, IL, United States.
  • Wang, S., Guan, K., Ainsworth, E., Zhang, C., Leakey, A., Zhou, Q., Margenot, A., Townsend, P., Jiang, C., (December 2022). AI-empowered hyperspectral sensing advances agricultural ground truthing across scales. [Conference presentation]. American Geophysical Union Annual Conference.
  • Wang, S., Guan, K., Zhang, C., Zhou, Q., Ainsworth, E., Margenot, A., Jiang, C., Peng, B., Wu, X. (December 2022). Cross-scale sensing of field-level crop residue cover and cover crops: integrating field observations, airborne hyperspectral imaging, and satellite data. [Conference presentation]. American Geophysical Union Annual Conference.
  • Wedow, J.M. (2022, July). AIFARMS Overview [Presentation]. The Internet of Things for Agriculture Summer Series, online. https://iot4ag.us/summerseries/
  • Wu.  J. (October 2022).Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning.[Conference presentation]. 31st ACM International Conference on Information & Knowledge Management.
  • Wu, J., He, J. (2022, August). Domain Adaptation with Dynamic Open-Set Targets [Conference presentation]. Knowledge discovery and data mining (KDD), Washington DC, United States.
  • Yu, C. (2022, August). Economic Incentives for Robotic Weed Control in Row Crop Agriculture [Conference presentation]. Agricultural and Applied Economics Association Meetings, Anaheim, CA, United States.
  • Zhou, Q., Guan, K., Wang, S., Jiang, C., Peng, B., Huang, Y. (December 2022). Quantifying field-level cover cropping in the U.S. Midwest using multi-source satellite data. [Conference presentation]. American Geophysical Union Annual Conference

2021

Publications:
  • Basso, B. (2021). Precision conservation for a changing climate. Nature Food, 2,322–323. https://doi.org/10.1038/s43016-021-00283-z
  • B. Basso, R. Martinez-Feria, R. A., Rill, L., Ritchie J. T. (2021). Contrasting long-term temperature trends reveal minor changes in projected potential evapotranspiration in the US Midwest. Nature Communications, 12, 1476. https://doi.org/10.1038/s41467-021-21763-7
  • Baquero A., Higuti V. A., Gasparino  M. V., Sivakumar A. N., Becker M., Chowdhary G. (2021). Multi-Sensor Fusion based Robust Row Following for Compact Agricultural Robots, Journal of Field Robotics. Journal of Field Robotics, 2, 1291-1319. https://doi.org/10.55417/fr.2022043
  • Graber, C., Tsai, G., Firman, M., Brostow, G., and Schwing, A. G. (2021). Panoptic Segmentation Forecasting, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), abs/2104.03962. 12517–12526. https://doi.org/10.48550/arXiv.2104.03962
  • Hu T.-T., Wang, J., Yeh, R. A., Schwing A, G. (2021). SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction From Video Data, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), abs/2105.08612. 1418–1428. https://doi.org/10.48550/arXiv.2105.08612
  • Jing, B., Tong, H., Zhu, Y. (2021). Network of Tensor Time Series. Proceedings of the Web Conference 2021 (WWW 2021), abs/2102.07736, 2425-2437. https://doi.org/10.1145/3442381.3449969
  • Khanna, M. (2021). Digital Transformation of the Agricultural Sector: Pathways, Drivers and Policy Implications, Applied Economic Perspectives and Policy, 43(4), 1221-1242. https://doi.org/10.1002/aepp.13103
  • Kamtikar, S., Marri, S., Walt, B., Uppalapati, N. K., Krishnan, G., Chowdhary, G. (2021). Towards Autonomous Berry Harvesting using Visual Servoing of Soft Continuum Arm, Proceedings of AI for Agriculture and Food Systems. https://openreview.net/forum?id=nmFQlTk6WpV.
  • Maestrini, A., Basso, B. (2021). Subfield crop yields and temporal stability in thousands of US Midwest fields, Precision Agriculture, 22, 1749-1767. https://doi.org/10.1007/s11119-021-09810-1
  • Northrup, D. L., Basso, B., Wang, M. Q., Morgan, C. L. S., Benfey, P. N. (2021). Novel technologies for emission reduction complement conservation agriculture to achieve negative emissions from row-crop production, Proceedings of the National Academy of Sciences, 118(28). https://doi.org/10.1073/pnas.2022666118
  • Ren, Z., Misra, I., Schwing, A. G., Girdhar, R. (2021). 3D Spatial Recognition Without Spatially Labeled 3D, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), abs/2105.06461, 13204–13213. https://doi.org/10.48550/arXiv.2105.06461
  • Shirke A., Saifuddin, A., Luthra, A., Li, J., Williams, T., Hu, X., Kotnana, A., Kocabalkanli, O., Ahuja, N., Green-Miller, A., Condotta, I., Dilger, R., Caesar, M. (2021). Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras, AgEng2021, abs/2111.10971. https://doi.org/10.48550/arXiv.2111.10971
  • Wang, S., Guan, K., Wang, Z., Ainsworth, E.A., Zheng, T., Townsend, P.A., Liu, N., Nafziger, E., Masters, M.D., Li, K., Wu, G. (2021). Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling. International Journal of Applied Earth Observation and Geoinformation, 105, 102617. https://doi.org/10.1016/j.jag.2021.102617
  • Wu, J., He, J. (2021) Indirect Invisible Poisoning Attacks on Domain Adaptation, SIGKDD Conference on Knowledge Discovery & Data Mining, 1852–1862. https://doi.org/10.1145/3447548.3467214
  • Williams, T., Green-Miller, A. (2021). 4 Engineered Resilience in Livestock for Improved Animal Welfare, Journal of Animal Science, 99(Supp. 3), 1. https://doi.org/10.1093/jas/skab235.000
  • Xie, J. Fernandes, S., Mayfield-Jones, D., Erice, G., Choi, M., Lipka, A., Leakey, A. (2021). Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping, Plant Physiology, 187(3). https://doi.org/10.1093/plphys/kiab299.
  • Zhuang, P., Koyejo, O., Schwing, A. G. (2021). Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation, International Conference on Learning Representations, abs/2102.01187. https://doi.org/10.48550/arXiv.2102.01187
Presentations:
  • Adve, V. (2021, October). Beyond ML in Agricultural Intelligence [Presentation]. Online.
  • Bernard, G.C. (2021, December). Agricultural robots and Autonomous farming: Ag Modernization to improve efficacy in agricultural production [Conference presentation]. Professional Agricultural Worker’s Conference, Online. 
  • Chowdhary, G. (2021, November). The robots are coming to you farm [Seminar]. Michigan State University, Robotics and Control seminar, Online.
  • Khanna, M. (2021, August). Digital Transformation for a Sustainable Agriculture in the US: Opportunities and Challenges [Conference presentation]. International Conference of Agricultural Economists, Online.
  • Khanna, M. (2021, September). Economic Incentives for Robotic Weed Control [Presentation]. PhenoRob Cluster of Excellence – University of Bonn, Online.
  • Kuhl, A.S., Guha, S., He, J., Tong, H., Margenot, A.J., Douglass, M., Kemner, J., Matamala, R. (2021, December). An AI Approach to Soil Health and Nutrient Management [Conference presentation]. American Geophysical Union Fall Meeting, New Orleans, LA, USA.
  • Leakey, A. (2021, September). Phenotyping stomatal anatomy and function [Virtual workshop]. Society for Experimental Biology Environmental Physiology Group, Virtual Workshop on Field and Laboratory Techniques, Online.
  • Margenot, A. (2021, December). AIFARMS – Soil Health and Monitory Thrust Summary [Conference presentation]. American Geophysical Union Fall Meeting, New Orleans, LA, United States.
  • Wang, H. (2021, November). Deep Active Learning for Agricultural Tasks [Flash talk]. Third International Workshop on Machine Learning for Cyber-Agricultural Systems, Online.
  • Wang, S., Guan, K., Zhou, Q., Zhang, C., Jiang, C., Li, K., Qin, Z., Ainsworth, E.A., Margenot, A.J., Schaefer, D. Gentry, L. (2021, December). Airborne hyperspectral imaging of cover crop outcomes and tillage intensity in croplands by machine learning and radiative transfer modeling [Conference presentation]. American Geophysical Union Fall Meeting, New Orleans, LA, USA.
  • Wu, J. (2021, November). Adaptive Transfer Learning for Plant Phenotyping [Conference presentation]. Third International Workshop on Machine Learning for Cyber-Agricultural Systems, Online.

2020

Publications:
  • Basso, B., Antle, J. (2020). Digital agriculture to design sustainable agricultural systems. Nature Sustainability, 3, 254–256. https://doi.org/10.1038/s41893-020-0510-0
  • Khanna, M. (2020). Digital Transformation of the Agricultural Sector: Pathways, Drivers and Policy Implications, Applied Economic Perspectives and Policy, 43(4), 1221-1242. https://doi.org/10.1002/aepp.13103
  • Liu, I-J., Yeh. R., Schwing, A.G. (2020). High-Throughput Synchronous Deep RL, Advances in Neural Information Processing Systems, 1432. 17070–17080.  https://ioujenliu.github.io/HTS-RL/
  • Ren, Z., Yeh, R., Schwing, A.G. (2020). Not All Unlabeled Data Are Equal: Learning to Weight Data in Semi-Supervised Learning, Conference on Neural Information Processing Systems,1828, 21786-21797. https://doi.org/10.48550/arXiv.2007.01293
  • Sun, R., Fang, T., Schwing, A.G. (2020). Towards a Better Global Loss Landscape of GANs, Advances in Neural Information Processing Systems. https://doi.org/10.48550/arXiv.2011.04926
Presentations:
  • Adve, V. (October 7, 2020). AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability. Virtual.
  • Adve, V. (November 19, 2020). 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.
  • Adve, V. (November 10, 2020). AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability. Virtual.
  • Leakey, A. (October 2020). The Phenomics of Stomata and Water Use Efficiency in C4 crops. Martin and Ruth Massengale Lecture to the Annual Meeting of the Crop Science Society of America.
  • Leakey, A. (December 2020). The Phenomics of Stomata and Water Use Efficiency in C4 crops. Virtual.

The Artificial Intelligence for Future Agricultural Resilience, Management, and Sustainability (AIFARMS) Institute is supported by the USDA National Institute of Food and Agriculture and the National Science Foundation.

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Functional Cookies enhance the performance and functionality of our websites but are non-essential to their use. These permanent Cookies allow our website to remember information from your previous visits, such as details you submitted before or your previously stated preferences. These Cookies may also be used to provide services you request, such as newsletters or publications. They may be first- or third-party Cookies that enable services we have added to our webpages. If you set your browser to block or delete Cookies, some or all of these services may not function properly.

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Targeting Cookies are used to deliver content tailored to your interests and may be temporary or permanent. They may also be first-party or third-party Cookies. Targeting Cookies are based on uniquely identifying your browser and device; they do not store information such as your name. The University may use targeting Cookies prepared by the University, its third-party contractors, or advertising partners to provide you with personalized University display advertising and promotional material about the University and its programs. The University may also allow third parties to place Cookies on your device that collect and use anonymous information about your visits to, and interactions with, our websites to personalize advertisements and promotional materials for University goods and services. Targeting Cookies may be used by our third-party contractors or our advertising partners to build a profile of your interests and show you relevant advertisements on other sites. We may share information about your use of our site with our social media, advertising, and analytics partners who may combine it with other information that you have provided to them or that they have collected from your use of their services. If you set your browser to block or delete Cookies, you will still see advertisements, but they will be less targeted to your interests.

Always Active