Publications
  • N. Ahmed, F. Esposito, N. Shakoor, "Bridging IoT Education Through Activities: A Game-Oriented Approach with Real-time Data Visualization", in IEEE Integrated STEM Education Conference 2024, Princeton, NJ, March 9, 2024, to appear!
  • N. Ahmed, N. Shakoor, "From Field to Cloud: IoT and Machine Learning Innovations in High-Throughput Phenotyping", to appear in: Springer book series "Lecture Notes in Networks and Systems" 2024.
  • B. Roy, V. Sagan, A. Haireti, M. Newcomb, R. Tuberosa, D. LeBauer, N. Shakoor, "Early Detection of Drought Stress in Durum Wheat Using Hyperspectral Imaging and Photosystem Sensing", Remote Sens. 2024, 16, 155, https://doi.org/10.3390/rs16010155
  • N. Ahmed, F. Esposito, O. Okafor, N. Shakoor, "SoftFarmNet: Reconfigurable Wi-Fi HaLow Networks for Precision Agriculture", in IEEE International Conference on Cloud Networking (IEEE CloudNet) 2023, to appear!
  • B. Gano, N. Ahmed, N. Shakoor, "Machine learning-based prediction of sorghum biomass from UAV multispectral imagery data," 2023 4th International Conference on Computing and Communication Systems (I3CS), Shillong, 2023, pp. 1-5, https://doi.org/10.1109/I3CS58314.2023.10127516
  • K. Dilmurat, V. Sagan, S. Moose, "AI-Driven Maize Yield Forecasting Using Unmanned Aerial Vehicle-Based Hyperspectral and LIDAR Data Fusion", ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3-2022, 2022, 193–199, https://doi.org/10.5194/isprs-annals-V-3-2022-193-2022
  • S. Bhadra, V. Sagan, et al., "PROSAIL-Net: A transfer learning-based dual stream neural network to estimate leaf chlorophyll and leaf angle of crops from UAV hyperspectral images", ISPRS Journal of Photogrammetry and Remote Sensing, 210, 2024, 1-24, https://doi.org/10.1016/j.isprsjprs.2024.02.020
  • M. Maimaitijiang, V. Sagan, S. Bhadra, C. Nguyen, T. C. Mockler, N. Shakoor, "A Fully Automated and Fast Approach for Canopy Cover Estimation Using Super High-Resolution Remote Sensing Imagery", ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3-2021, 2021, 219–226, https://doi.org/10.5194/isprs-annals-V-3-2021-219-2021
  • S. Bhadra, V. Sagan, C. Nguyen, M. Braud, A. L. Eveland, T. C. Mockler, "Automatic Extraction of Solar and Sensor Imaging Geometry from UAV-Borne Push-Broom Hyperspectral Camera", ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3-2022, 2022, 131–137, https://doi.org/10.5194/isprs-annals-V-3-2022-131-2022
  • V. Sagan, M. Maimaitijiang, S. Paheding, S. Bhadra, et al., "Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data", IEEE Transactions on Geoscience and Remote Sensing, 60, 2022, pp. 1-20, https://doi.org/10.1109/TGRS.2021.3091409
Mission

Empowering agriculture through innovation, FieldDock aims to revolutionize farm management and crop improvement by integrating autonomous UAVs, sensor networks, and advanced analytics into a cohesive, user-friendly platform.

Objectives

  • Provide real-time, data-driven insights for precise crop performance tracking and management.
  • Enhance agricultural efficiency and productivity through autonomous UAV monitoring and edge computing solutions.
  • Foster sustainable farming practices by optimizing resource use and reducing environmental impact.
  • Deliver intuitive, accessible tools for farmers and researchers to make informed decisions and drive agricultural advancements.

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