Skip to content
- Baquero A., Higuti V. A., Gasparino M. V., Sivakumar A. N., Becker M., Chowdhary G. Multi-Sensor Fusion based Robust Row Following for Compact Agricultural Robots, Journal of Field Robotics, accepted February 2022. https://doi.org/10.55417/fr.2022043
- Kamtikar S., Marri S., Walt B., Uppalapati K. N., Krishnan G., Chowdhary G., Visual Servoing for Pose Control of Soft Continuum Arm in a Structured Environment, Jointly published in Robotics and Automation Letters (RAL), and IEEE 5th International Conference on Soft Robotics (RoboSoft), Edinburgh, Scotland, UK, April 2022. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9726901
- Tuan Lai, Heng Ji and ChengXiang Zhai. 2022. Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking. Proc. The 60th Annual Meeting of the Association for Computational Linguistics (ACL2022) Findings. https://arxiv.org/pdf/2202.13404.pdf
- J. He. A Unified Meta-Learning Framework for Dynamic Transfer Learning. IJCAI-ECAI 2022. https://arxiv.org/pdf/2207.01784.pdf
- Pedro Cisneros-Velarde, Boxiang Lyu, Sanmi Koyejo, Mladen Kolar. “One Policy is Enough: Parallel Exploration with a Single Policy is Minimax Optimal for Reward-Free Reinforcement Learning”, Arxiv, 2022. https://arxiv.org/pdf/2205.15891.pdf
- Khanna, M. and Miao, R., Inducing the adoption of emerging technologies for sustainable intensification of food and renewable energy production: insights from applied economics*. Aust J Agric Resour Econ, 66: 1-23, 2022. https://doi.org/10.1111/1467-8489.12461
- Khanna, M., Atallah. S., Kar. S., Sharma. B., Wu, L., Yu. C., Chowdhary, G., Soman. C., Guan. K. “Digital transformation for a sustainable agriculture in the United States: Opportunities and challenges”. The Journal of the International Association of Agricultural Economists, 2022. https://doi.org/10.1111/agec.12733
- Gregory C. Bernard, Olga Bolden-Tiller, Marceline Egnin, Conrad Bonsi, Amir McKinstry, Zaire Landon, Bob YY. Chen Inocent Ritte, Tia Archie, MD Shafait Chowdhury, Capri Charleston, Alexandria Turner, Adrianne Brown, Osagie Idehen, Ivi Mitchell, Jasmine Boone, Christian Peterson, and Andrea Lockett The Use of Autonomous Robots to Address Labor Demands and Improve Efficacy in Agriculture. C.O.J. Rob Artificial Intel. 1(5). C.O.J.R.A. 000523. 2022.
- Feb 2022 130th Annual Small Farmer’s Conference – Dean Olga Bolden-Tiller highlights the research performed at TU, including the adoption and incorporation of autonomous tools and collaboration with the Univ. of Illinois.
- Presentation given to 30 high school students on campus at the U of I in attendance for our Ag Innoators Challenge Training in February of 2022. Hosted at the Siebel Center for Design.
- Tong. H. ‘NetFair: Toward the Why Question of Network Mining’ at Artificial Intelligence, Machine Learning and Data Science World Forum 2022.
- On April 14th, Supratik Guha was invited to give a talk on our work on IoT sensor networks for soil and water quality to the Environmental Engineering Department at U Wisconsin-Madison.
- Yu, C., Economic Incentives for Robotic Weed Control in Row Crop Agriculture, Agricultural and Applied Economics Association Meetings, Anaheim, CA, August 2, 2022
- Yu, C., Economic Incentives for Robotic Weed Control in Row Crop Agriculture, The Southern Economic Association 92nd Annual Meeting, Fort Lauderdale, FL, November 19, 2022
- J. Wu, and J. He. Domain Adaptation with Dynamic Open-Set Targets. KDD 2022
- J. Wu and J. He, “Indirect Invisible Poisoning Attacks on Domain Adaptation,” SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1852–1862, Aug. 2021, https://doi.org/10.1145/3447548.3467214
- J. Xie et al., “Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping,” Plant Physiology, vol. kiab299, Jul. 2021, doi: https://doi.org/10.1093/plphys/kiab299.
- D. Northrup, B. Basso, M. Q. Wang, C. L. S. Morgan, and P. Benfey, “Novel technologies for emission reduction complement conservation agriculture to achieve negative emissions from row-crop production,” Proceedings of the National Academy of Sciences, vol. 118, no. 28, Jul. 2021, doi: https://doi.org/10.1073/pnas.2022666118
- T.-T. Hu, J. Wang, R. A. Yeh, and A. G. Schwing, “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), pp. 1418–1428, Jun. 2021 [Online]. Available: https://arxiv.org/abs/2105.08612
- C. Graber, G. Tsai, M. Firman, G. Brostow, and A. G. Schwing, “Panoptic Segmentation Forecasting,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12517–12526, Jun. 2021 [Online]. Available: https://arxiv.org/abs/2104.03962
- Basso, B. Precision conservation for a changing climate. Nat Food 2, 322–323 (2021). https://doi.org/10.1038/s43016-021-00283-z
- A. Maestrini and B. Basso, “Subfield crop yields and temporal stability in thousands of US Midwest fields,” Precision Agriculture, May 2021 [Online]. Available: https://link.springer.com/article/10.1007/s11119-021-09810-1
- B. JIng, H. Tong, and Y. Zhu, “Network of Tensor Time Series,” Proceedings of the Web Conference 2021, pp. 2425–2437, Apr. 2021, doi: https://doi.org/10.1145/3442381.3449969.
- B. Basso, R. Martinez-Feria, L. Rill, and J. Ritchie, “Contrasting long-term temperature trends reveal minor changes in projected potential evapotranspiration in the US Midwest,” Nature Communications, vol. 12, p. 1476, Mar. 2021, doi: https://doi.org/10.1038/s41467-021-21763-7.
- B. Basso, “Reducing the Health Impacts of the Nitrogen Problem: Defining the Problem,” Virtual, Jan. 28, 2021 [Online]. Available: https://www.nationalacademies.org/event/01-28-2021/reducing-health-impacts-of-reactive-nitrogen-in-ground-and-surface-water-from-agricultural-sources-an-environmental-health-matters-workshop-to-identify-opportunities-for-leadership-workshop-series-1
- Wang, S., Guan, K., Wang, Z., Ainsworth, E.A., Zheng, T., Townsend, P.A., Liu, N., Nafziger, E., Masters, M.D., Li, K. and 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, p.102617. 10.1016/j.jag.2021.102617
- 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, p.112914. 10.1016/j.rse.2022.112914
- 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.
- 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. Available: https://www.arxiv-vanity.com/papers/2111.10971/
- Baoyu Jing, Hanghang Tong, Yada Zhu: Network of Tensor Time Series. WWW 2021: 2425-2437. Available: https://arxiv.org/pdf/2102.07736.pdf
- 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) https://doi.org/10.1093/jas/skab235.000
- Wang, S., Guan, K., Zhou, Q., Zhang, C., Jiang, C., Li, K., Qin, Z., Ainsworth, E.A., Margenot, A.J., Schaefer, D. and Gentry, L., 2021, December. Airborne hyperspectral imaging of cover crop outcomes and tillage intensity in croplands by machine learning and radiative transfer modeling. In AGU Fall Meeting 2021. AGU. https://ui.adsabs.harvard.edu/abs/2021AGUFM.B55K1320W/abstract
- Khanna, M., Digital Transformation of the Agricultural Sector: Pathways, Drivers and Policy Implications. Applied Economic Perspectives and Policy, 43: 1221-1242, 2021. https://onlinelibrary.wiley.com/doi/epdf/10.1002/aepp.13103
- Z. Ren, I. Misra, A. G. Schwing, and R. Girdhar, “3D Spatial Recognition Without Spatially Labeled 3D,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13204–13213, Jun. 2021 [Online]. Available: https://arxiv.org/abs/2105.06461
- P. Zhuang, O. Koyejo, and A. G. Schwing, “Enjoy Your Editing: Controllable GANs for Image Editing via Latent Space Navigation,” International Conference on Learning Representations, May 2021 [Online]. Available: https://arxiv.org/abs/2102.01187
- V. Adve, “Beyond ML in Agricultural Intelligence,” Virtual, Oct. 21, 2021.
- M. Khanna, “Economic Incentives for Robotic Weed Control,” Virtual – University of Bonn, Sep. 24, 2021 [Online]. Available: https://www.phenorob.de/
- R. Finger, R. Huber, Y. Wang, and M. Khanna, “Digital innovations for more sustainable agricultural landscapes,” Virtual – Berlin, Sep. 20, 2021.
- M. Khanna, “Digital Transformation for a Sustainable Agriculture in the US: Opportunities and Challenges,” Virtual, Aug. 29, 2021 [Online]. Available: https://www.cgiar.org/news-events/event/international-conference-of-agricultural-economists-icae-2021/
- T. Williams and A. Green-Miller, “Engineered Resilience in Livestock for Improved Animal Welfare,” Louisville, KY, Jul. 16, 2021 [Online]. Available: https://www.asas.org/meetings/annual-2021
- A. Shirke et al., “Tracking Grow-Finish Pigs Across Large Pens Using Multiple Cameras,” Portugal (Virtual), Jul. 2021.
- A. Schwing, “AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability,” CVPR 2021 – Virtual, Jun. 19, 2021.
- V. Adve, “AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability,” Virtual, Jun. 03, 2021.
- 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. Virtual, Jun. 2021 [Online]. Available: https://cvpr2021.thecvf.com/
- V. Adve, “Computational Needs for the AIFARMS National AI Institute,” Apr. 07, 2021.
- V. Adve, “AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability,” Virtual, Mar. 23, 2021.
- T. Williams, “The Art and Science of Black Farming, STEM Illinois Communiversity Think & Do Tank” Virtual, Feb. 27, 2021.
- E. Ainsworth, “Using hyperspectral reflectance to estimate and map photosynthesis in a soybean NAM population,” Virtual, Feb. 2021.
- B. Basso., R. Chandra., A. Marklein., C. W. Rice., J. W. Tiegje., K. Todd-Brown., R. Vargas., “Exploring a Dynamic Soil INformation System: Proceedings of a Workshop,” Washington DC, 2021.
- B. Basso, “Digital Agriculture to Reduce Nitrogen Losses across the U.S. Corn Belt,” Virtual, 2021.
- J. Wu. Adaptive Transfer Learning for Plant Phenotyping. Third International Workshop on Machine Learning for Cyber-Agricultural Systems, Nov 2, 2021
- The robots are coming to you farm, Michigan State University, Robotics and Control Seminar, November 2021. Girish Chowdhary.
- November 2, 2021, flash talk by Haonan Wang: Deep Active Learning for Agricultural Tasks, Third International Workshop on Machine Learning for Cyber-Agricultural Systems
- February 25, 2022, keynote talk by Jingrui He: Towards Understanding Rare Categories on Graphs, the International Workshop on Machine Learning on Graphs, WSDM 2022
- Kuhl, A.S., Guha, S., He, J., Tong, H., Margenot, A.J., Douglass, M., Kemner, J., and R. Matamala, An AI Approach to Soil Health and Nutrient Management. American Geophysical Union Fall Meeting, New Orleans, LA, USA, December 13-17, 2021.
- X. Hu and N. Ahuja, ICCV 2021, HumanPose Sequence Estimation and Recognition.
- M. Chatterjee, A. Cherian, N. Ahuja, ICCV 2021: Audio-Visual Fusion.
- 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
- Bruno Basso, 2021 Columbia University, Integrating crop models, AI, and sensing for scaling sustainable agricultural systems.
- Bruno Basso, 2021 AgMIP annual meeting, Modeling Circular Agricultural Systems, Columbia University
- Andrew Leakey, The Phenomics of Stomata and Water Use Efficiency in C4 crops (March 2021). UIUC Department of Plant Biology colloquium
- Andrew Leakey, The Phenomics of Stomata and Water Use Efficiency in C4 crops (April 2021). DOE BRC Sorghum workshop
- 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
- Andrew Leakey, Phenotyping stomatal anatomy and function (Sept 2021) Society for Experimental Biology Environmental Physiology Group, Virtual Workshop on Field and Laboratory Techniques.
- Alex Schwing, “AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability,” Vision for Agriculture Workshop at CVPR, 2021.
- I.-J. Liu, R. A. Yeh, and A. G. Schwing, “High-Throughput Synchronous Deep RL,” NeurIPS 2020, Dec. 2020 [Online]. Available: https://ioujenliu.github.io/HTS-RL/
- M. Khanna, “Digital Transformation of the Agricultural Sector: Pathways, Drivers and Policy Implications,” Applied Economic Perspectives and Policy, Oct. 2020, doi: https://doi.org/10.1002/aepp.13103.
- Z. Ren, R. A. Yeh, and Schwing, “Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning,” NeurIPS 2020, vol. 2, Oct. 2020 [Online]. Available: https://arxiv.org/abs/2007.01293
- B. Basso and J. Antle, “Digital agriculture to design sustainable agricultural systems,” Nature Sustainability, vol. 3, pp. 254–256, Apr. 2020 [Online]. Available: https://www.nature.com/articles/s41893-020-0510-0
- R. Sun, T. Fang, and A. G. Schwing, “Towards a Better Global Loss Landscape of GANs,” Neur IPS 2020, 2020 [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/738a6457be8432bab553e21b4235dd97-Paper.pdf
- 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,” Agricultural Economics.
- I.-J. Liu, R. Yeh, A.G. Schwing. “High-Throughput Synchronous RL” NIPS’20: Proceedings of the 34th International Conference on Neural Information Processing Systems. December 2020 Article No.: 1432 Pages 17070–17080
- Ren, Zhongzheng, et al. “Not All Unlabeled Data Are Equal: Learning to Weight Data in Semi-Supervised Learning.” ArXiv.org, 29 Oct. 2020, https://arxiv.org/abs/2007.01293.
- A. Leakey, “The Phenomics of Stomata and Water Use Efficiency in C4 crops,” Virtual, Dec. 2020.
- V. 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, Nov. 19, 2020.
- V. Adve, “AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability,” Virtual, Nov. 10, 2020.
- V. Adve, “AIFARMS: Artificial Intelligence for Future Agricultural Resilience, Management and Sustainability,” Virtual, Oct. 07, 2020.
- 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 AmericaAndrew Leakey, The Phenomics of Stomata and Water Use Efficiency in C4 crops (Feb 2021). University of Missouri Interdisciplinary Plant Group seminar