B030
Data-Driven Agriculture: Remote Sensing and Machine Learning Solutions for Food Security
Session ID#: 248076
Session Description:
With an increasing global population, limited arable land, and anticipated extreme climate conditions, it is imperative to optimize food production. We currently reside in a data-rich era with an extensive array of datasets including those with high spatial resolutions (e.g., Planet Labs SuperDoves and Uncrewed Aircraft Systems (UASs)), and extensive temporal archives (e.g., Landsat, Sentinel). These datasets augment our capacity for comprehensive studies of agricultural food systems. By leveraging advanced methodologies for big-data analytics such as data fusion, novel vegetation indices, time-series analyses, dimensionality reduction, and machine learning, including deep learning and artificial intelligence techniques, we can harness the full potential of such datasets and navigate the complexities of modern agricultural landscapes for optimized decision-making. We invite abstract submissions that leverage these datasets, technologies, and methods to inform decision-making on issues of global food and water security, agricultural resource management, and sustainability.
Index Terms:
0402 Agricultural systems [BIOGEOSCIENCES]
0430 Computational methods and data processing [BIOGEOSCIENCES]
0452 Instruments and techniques [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]
Primary Convener: Alifu Haireti, Saint Louis University, Remote Sensing Laboratory, Department of Earth, Environmental and Geospatial Science, St. Louis, United States
Conveners: Itiya Aneece, USGS Western Geographic Science Center, Flagstaff, United States and Jyoti Jennewein, USDA, Agricultural Research Service, Sustainable Agricultural Systems Laboratory, Beltsville, United States
Integrating Crop Dynamics and Pesticide Exposure Zones to Identify Organic Farming Potential in Vietnam’s Mekong River Delta (1844990)
Nick Kupfer, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Aachen, GERMANY, Linda Klamann, Agrosphere Institute (IBG-3) Forschungszentrum Jülich, Deutschland, Germany, Carsten Montzka, Agrosphere Institute (IBG-3), Research Centre Juelich, Juelich, Germany and Tuan Quoc Vo, Can Tho University, Department of Land Resource Management, Can Tho, Vietnam
Integrating High-Resolution UAV Imagery with Vertical Soil Carbon Change Modeling (1868691)
Bishal Roy, Saint Louis University, Earth, Environmental and Geospatial Science, Saint Louis, United States and Vasit Sagan, Saint Louis University, Remote Sensing Laboratory, Department of Earth, Environmental and Geospatial Science, St. Louis, United States
Multiscale Analysis of Fractional Vegetation Cover (FVC) and Land Surface Temperature (LST) Using Images from UAV and Satellite Platforms in Kenyan Agricultural Landscapes (1884404)
Ashfak Mahmud1, Juuso Tuure2, Mwadime Mjomba3, Petri Pellikka1,4 and Janne Heiskanen1, (1)University of Helsinki, Department of Geosciences and Geography, Helsinki, Finland, (2)University of Helsinki, Department of Agricultural Sciences, Helsinki, Finland, (3)Taita Environmental Research and Resource Arc, Wundanyi, Kenya, (4)University of Nairobi, Wangari Maathai Institute for Environmental and Peace Studies, Nairobi, Kenya
Mapping Urban and Peri-Urban Farming in New Jersey: A Multi-Method Evaluation of OBIA and Deep Learning Approaches (1893444)
MD Shahinoor Rahman1, Samiha Nuzhat2, Asif Ishtiaque2 and Mahbubur Meenar3, (1)Louisiana State University Health Sciences Center, School of Public Health, New Orleans, United States, (2)Missouri State University, School of Earth, Environment and Sustainability, Springfield, United States, (3)Rowan University, Department of Geography, Planning, and Sustainability, Glassboro, NJ, United States
Domain‑Adversarial Learning Improves Soybean Root Biomass Estimation Across Contrasting Climate Regimes Using Multi‑Temporal SkySat Imagery (1900987)
Alifu Haireti1, Vasit Sagan1, Abuduwanli Maiwulanjiang2, Supria Sarkar2, Heng Ye3, Dhandapani Raju3 and Henry Nguyen3, (1)Saint Louis University, Remote Sensing Laboratory, Department of Earth, Environmental and Geospatial Science, St. Louis, United States, (2)Saint Louis University, Department of Earth and Atmospheric Sciences, Saint Louis, United States, (3)University of Missouri, Division of Plant Science and Technology, Columbia, United States
Mapping Agricultural Abandonment in the Lesser Himalayas with Remote Sensing and Deep Learning (1924814)
Rachel Whiteside, Cornell University, Ithaca, NY, United States, Ginger Allington, Cornell University, Ithaca, United States and Kade Keranen, Cornell University, Department of Natural Resources and the Environment, Ithaca, United States
Integrating UAV LiDAR and Multispectral Imagery for 3D Characterization of Soybean Canopy and Canopy Cover Estimation (1928924)
Nirab Shrestha1, Vasit Sagan1, Alifu Haireti1, Derek Tesser1, Ashutosh Pawar1, Cagri Gul1, Heng Ye2, Dhandapani Raju2 and Henry Nguyen2, (1)Saint Louis University, Remote Sensing Laboratory, Department of Earth, Environmental and Geospatial Science, St. Louis, United States, (2)University of Missouri, Division of Plant Science and Technology, Columbia, United States
Edge Effects on Intra‑field Crop Yield Variability (1931841)
Samar Ranjit1, Dr. Eunsang Cho, Ph.D.2, Simon Kraatz3, Annelise Holguin4, Dr. Jisung Chang3, Feng Gao5, Martha Anderson3, David Johnson6, Haoteng Zhao7 and Richard Cirone6,8, (1)Texas State University, Department of Computer Science, San Marcos, United States, (2)Texas State University, Department of Civil Engineering, San Marcos, United States, (3)USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States, (4)Texas State University, Geography and Environmental Studies, San Marcos, United States, (5)USDA, ARS, Beltsville, United States, (6)USDA ARS Hydrology and Remote Sensing Laboratory, Beltsville, United States, (7)USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, United States, (8)Iowa State University, Ames, IA, United States
Development of a Web-Based System for Soybean and Corn Monitoring in the United States Using Pre-Harvest Crop Classification and Satellite-Derived NIRv Index (1942099)
Jaeyoung Jung1, Seungwon Seok1, Miso Kim2, Solhee Kim2, Taegon Kim2, Weiguo Yu3, Youngryel Ryu4 and Kyungdo Lee5, (1)Jeonbuk National University, Department of Agricultural Engineering, Jeonju, South Korea, (2)Jeonbuk National University, Department of Smart Farm, Jeonju, South Korea, (3)Seoul National University, Research Institute of Agriculture and Life Sciences, Seoul, South Korea, (4)Seoul National University, Department of Landscape Architecture and Rural Systems Engineering, Seoul, South Korea, (5)National Agricultural Satellite Center, National Institute of Agricultural Sciences, Jeonju-si, South Korea
UAV-Based Multispectral, and LiDAR Remote Sensing for Mapping and Evaluating Fine-Scale Variations and Temporal Dynamics of Crop Growth Parameters in Corn. (1951832)
Bikash Ghimire1, Jean Rugandirababisha2, Dr. Ranjani Kulawardhana, PhD2, Sumantra Chatterjee2, Dedrick Davis2, Monday Mbila PhD2, Xianyan Kuang2 and Wubishet Tadesse2, (1)Alabama A & M University, Department of Natural Resources and Environmental Sciences, Normal, AL, United States, (2)Alabama A & M University, Department of Natural Resources and Environmental Sciences, Normal, United States
Africa’s Fuel Accessibility: A Travel-Time Analysis (1957666)
Mansoor D Leh1, Darshana Wickramasinghe2, Janavathsasarma Baskarakurukkal3, Naga Manohar Velpuri2, Petra Schmitter2 and Abdulkarim Seid4, (1)International Water Management Institute, Colombo, Sri Lanka, (2)International Water Management Institute (IWMI), Colombo, Sri Lanka, (3)International Water Management Institute, Water Data Science, Colombo, Sri Lanka, (4)International Water Management Institute, ADDIS ABABA, Ethiopia
Non-Destructive Estimation of Carrot Yield Using Ground Penetrating Radar: A Precision Agriculture Approach (1965905)
Milad Vahidi, Ph.D. student, School of Plant and Environmental Sciences (SPES), Blacksburg, United States, Roshan Saud, Graduate research assistant at School of Plant and Environmental Sciences (SPES), Crop and Soil Sciences, Blacksburg, United States, Sanaz Shafian, Assistant Professor, School of Plant and Environmental Sciences (SPES), Blacksburg, United States, John Fike, Virginia Polytechnic Institute and State University, School of Plant and Environmental Sciences, Blacksburg, United States, Gan Huijie, Assistant Professor, School of Plant and Environmental Sciences (SPES), Crop and Soil Sciences, Blacksburg, United States and John E Barrett, Virginia Tech, Biological Sciences Department, Blacksburg, VA, United States
Machine Learning to Understand Farmer Decision-Making in Prevented Planting (1973895)
Kyongho Son1, Jillian M Deines2, Fengwei Hung3 and James Yoon2, (1)Pacific Northwest National Laboratory, Richland, WA, United States, (2)Pacific Northwest National Laboratory, Richland, United States, (3)Arizona State University, School of Sustainability, Tempe, United States
OurEnergyLandscape: A Harmonized Spatiotemporal Inventory of Solar Infrastructure and its Impacts on Land, Food, and Ecosystems in North America (1974689)
Jacob T. Stid1, Anthony D Kendall1, James Bingaman1, Aster Ellsworth2, Charles Bonten3, Jeremy Rapp1 and Annick Anctil4, (1)Michigan State University, Department of Earth and Environmental Sciences, East Lansing, MI, United States, (2)Michigan State University, Department of Earth and Environmental Sciences, East Lansing, United States, (3)Michigan State University, Department of Earth and Environmental Science, East Lansing, United States, (4)Michigan State University, Civil and Environmental Engineering, East Lansing, United States
Precision Yield Prediction Using Statistical and Machine Learning Methods for Small Holder Cotton Farming (1978247)
Maruf Hossain1, Sudip Sen2, Mst. Malihatun Nesa2, Md. Farhadur Rahman2, Md. Ashiquzzaman2, Abdul Kaium Tuhin2 and Hasan Muhammad Abdullah2, (1)Louisiana State University, Baton Rouge, LA, United States, (2)Gazipur Agricultural University, Department of Agroforestry and Environment, Gazipur, Bangladesh
Pinpointing High-Yield Climate Windows via a Satellite-Powered RF-GDM Framework for Four Staple Crops (1980239)
Seyed Babak Haji Seyed Asadollah1,2, Antonio Jodar-Abellan3 and Miguel Ángel Pardo Picazo1, (1)University of Alicante, Civil engineering, Alicante, Spain, (2)SUNY College of Environmental Science and Forestry, Environmental Resources Engineering, Syracuse, United States, (3)Spanish National Research Council, Soil and Water Conservation Research Group, Centre for Applied Soil Science and Biology of the Segura, Zaragoza, Spain
Space to Ground: Remote Sensing and Machine Learning Expose Soil Nutrient Loss from Stubble Burning (1992906)
Jayantifull Hoojon, Indian Institute of Technology Roorkee, Roorkee, India, Mukund Narayanan, Indian Institute of Technology Roorkee, Water Resources Development and Management, Roorkee, India and Idhayachandhiran Ilampooranan, Indian Institute of Technology Roorkee, Water Resources Development and Mangement, Roorkee, India
Studying the effect of climatic variables on wild blueberry productivity and modelling yield using MODIS photosynthetic productivity products (1996212)
Kallol Barai1, Parinaz Rahimzadeh-Bajgiran2, Ittai Herrmann3 and Yongjiang Zhang1, (1)University of Maine, School of Biology and Ecology, Orono, United States, (2)University of Maine, School of Forest Resources, Orono, United States, (3)The Hebrew University of Jerusalem, The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Rehovot, Israel
Accessible AI for Agriculture: Improving Land Cover Mapping with CNNs (1998409)
Siobhan Light, University of California Santa Cruz, Santa Cruz, CA, United States, Amy McNally, NASA Goddard Space Flight Center, Greenbelt, United States, Woubet G. Alemu, South Dakota State University, Geospatial Sciences Center of Excellence, Brookings, United States, Margaret Wooten, NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, United States, Jordan Caraballo-Vega, NASA Goddard Space Flight Center, Data Science Group, Greenbelt, United States, Christopher S R Neigh, NASA Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD, United States and amy mcnally
