B33F:
Smart Farming Enabled by Remotely Sensed Big Data Posters


Submit an Abstract to this Session


Session ID#: 56814

Session Description:
Smart Farming is currently driving revolution in agriculture, aiming at more productive and sustainable production through precise and resource-efficient farming decisions. Remotely sensed Big Data from various satellite platforms, small unmanned aerial systems, airborne systems, in situ and proximal sensors, brings both challenges and opportunities for Smart Farming which requires high spatial resolution and near real-time mapping capabilities. Successes in crop health monitoring, stress identification, soil mapping, fertilizer and irrigation advisories, yield prediction and more have been achieved. This session seeks contributions across government institutions, universities, private corporations, and other nonprofit organizations. It will focus on showcasing research methodologies and applications for the use of high spatial resolution or high temporal frequency remotely sensed Big Data for Smart Farming applications. We invite you to share findings throughout the chain of data collection, storage, transfer, transformation, analytics and discuss how to achieve the goal of more productive and sustainable agriculture production.
Primary Convener:  Yun Yang, University of Maryland College Park, ESSIC, College Park, MD, United States
Conveners:  Martha Anderson, USDA ARS, Hydrology and Remote Sensing Lab, Beltsville, MD, United States, Xiaoyuan Yang, The Climate Corporation San Francisco, San Francisco, CA, United States and Michael H Cosh, U.S. Department of Agriculture ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States
Primary Liaison:  Yun Yang, University of Maryland College Park, ESSIC, College Park, MD, United States
Chairs:  Yun Yang, University of Maryland College Park, ESSIC, College Park, MD, United States, Xiaoyuan Yang, The Climate Corporation San Francisco, San Francisco, CA, United States, Martha Anderson, USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States and Michael H Cosh, U.S. Department of Agriculture ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States
OSPA Liaison:  Xiaoyuan Yang, The Climate Corporation San Francisco, San Francisco, CA, United States

Cross-Listed:
  • EP - Earth and Planetary Surface Processes
  • IN - Earth and Space Science Informatics
Index Terms:

0402 Agricultural systems [BIOGEOSCIENCES]
0434 Data sets [BIOGEOSCIENCES]
0480 Remote sensing [BIOGEOSCIENCES]
1910 Data assimilation, integration and fusion [INFORMATICS]

Abstracts Submitted to this Session:

Data Driven Almond Yield Prediction at the Orchard Level with a Stochastic Gradient Boosting Method (422576)
Zhou Zhang1, Yufang Jin2, Bin Chen3 and Patrick Brown1, (1)University of California, Davis, Davis, CA, United States, (2)University of California Davis, Department of Land, Air, and Water Resources, Davis, United States, (3)University of California Davis, Department of Land, Air, and Water Resources, Davis, CA, United States
Microtopography and Crop Vigor Changes Assessment Using Time Series of UAS Derived Data. (422064)
Justyna Jeziorska, North Carolina State University Raleigh, Raleigh, NC, United States, Kellyn Montgomery, North Carolina State University Raleigh, Center for Geospatial Analytics, Raleigh, United States and Helena Mitasova, North Carolina State Univ, Marine, Earth, and Atmos. Sciences, Raleigh, NC, United States
3D Dynamic Morphological Structure of a Crop Captured from a UAV. (409245)
Matteo Giuseppe Ziliani, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, Kasper Johansen, King Abdullah University of Science and Technology, WDRC, Thuwal, Saudi Arabia, Mark Tester, King Abdullah University of Science and Technology, Center for Desert Agriculture, The Salt Lab, Thuwal, Saudi Arabia and Matthew McCabe, King Abdullah University of Science and Technology, Hydrology, Agriculture and Land Observation (HALO) Laboratory, Division of Biological and Environmental Sciences and Engineering, Thuwal, Saudi Arabia
A Comparative Analysis of sUAS Platforms and Sensors for Agricultural Management (456079)
Carolyn Bittner1, Lindsay Barbieri1 and Gillian L Galford2, (1)University of Vermont, Burlington, VT, United States, (2)University of Vermont, Rubenstein School of Environment and Natural Resources, Burlington, VT, United States
A Deep Learning Pipeline for High-Throughput Phenotyping from LiDAR Data (354134)
Shichao Jin1, Yanjun Su2, Fangfang Wu1, Shang Gao3, Shuxin Pang1 and Qinghua Guo4, (1)Institute of Botany, Chinese Academy of Sciences, Beijing, China, (2)Institute of Botany, Chinese Academy of Sciences, State Key Laboratory of Vegetation and Environmental Change, Beijing, China, (3)IB Institute of Botany, Chinese Academy of Sciences, Beijing, China, (4)Peking University, Beijing, China
A Drought Monitoring Index Based on Near Infrared and Red Spectral Space (427783)
Jieyun Zhang, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China and Qingling Zhang, Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences, Urumqi, China
A New Unmanned Aircraft System-Assisted Optical Trapezoid Model for Soil Moisture Assessment (366323)
Ebrahim Babaeian1, Maria Newcomb2, Richard Ward2, Morteza Sadeghi3, Scott B Jones3 and Markus Tuller4, (1)University of Florida, Soil and Water Sciences Department, Gainesville, United States, (2)University of Arizona, School of Plant Sciences, Maricopa, AZ, United States, (3)Utah State University, Logan, UT, United States, (4)Department of Environmental Science, The University of Arizona, Tucson, United States
A remote-sensing estimate of organic-rich topsoil erosion in the corn belt region of the midwestern United States (392822)
Evan Thaler, University of Massachusetts Amherst, Department of Geosciences, Amherst, MA, United States, Isaac J Larsen, University of Massachusetts-Amherst, Department of Earth, Geographic, and Climate Sciences, Amherst, United States and Qian Yu, Univ. of Massachusetts-Amherst, Department of Earth, Geographic, and Climate Sciences, Amherst, United States
An open-source tool to enable regional and field-scale ET estimates from Big Data (445622)
Mitchell A Schull, Earth System Science Interdisciplinary Center, COLLEGE PARK, MD, United States, Christopher Hain, NASA Marshall Space Flight Center, Earth Science Office, Huntsville, United States, Martha Anderson, USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States, Feng Gao, USDA, ARS, Beltsville, United States, Xiwu Zhan, NOAA/NESDIS/STAR, College Park, MD, United States and Christopher M U Neale, University of Nebraska, Daugherty Water for Food Global Institute, Lincoln, NE, United States
Application of pre-trained deep learning models for sorghum panicle characterization (451502)
Lonesome Malambo1, Sorin C Popescu2, David W Horne3, Nicholas Ace Pugh3 and William Rooney4, (1)Texas A&M University College Station, Department of Ecology and Conservation Biology, College Station, TX, United States, (2)Texas A&M University, Department of Ecology and Conservation Biology, College Station, United States, (3)Soil & Crop sciences, Texas A&M University, College Station, United States, (4)Texas A&M University, Department of Soil and Crop Sciences, College Station, TX, United States
Comparing the effects of natural vegetation and artificial plantation on soil structure in a geological hazard-prone region (405741)
Xia Wang, Lanzhou University, Lanzhou, China
Estimation of Stomatal Conductance using the Crop Water Stress Index through Thermal Infrared Image for Application of Smart Farming System (427291)
Hoejeong Jeong1, Jae-Hyun Ryu1, Dohyeok Oh1 and Jaeil Cho2, (1)Chonnam National University, Gwangju, Korea, Republic of (South), (2)Chonnam National University, Gwangju, South Korea
Evaluation of Reflectance Retrieval Methods for Hyperspectral Data Series using a UAV-based Pushbroom Camera (392047)
Yoseline Angel, King Abdullah University of Science and Technology, Hydrology and Land Observation Group (HALO), Division of Biological and Environmental Science & Engineering (BESE), Thuwal, Saudi Arabia, Rasmus Houborg, South Dakota State University, Geospatial Sciences Center of Excellence (GSCE), Brookings, SD, United States and Matthew McCabe, King Abdullah University of Science and Technology, Hydrology, Agriculture and Land Observation (HALO) Laboratory, Division of Biological and Environmental Sciences and Engineering, Thuwal, Saudi Arabia
Examining salinity induced plant stress using UAV-based Land Surface Temperature (434369)
Oliver Lopez1, Yoann Malbeteau2, Bruno Aragon1, Samir Al-Mashharawi3, Mitchell Morton1, Mark Tester4 and Matthew McCabe3, (1)King Abdullah University of Science and Technology, Thuwal, Saudi Arabia, (2)Mohammed VI Polytechnic University, Ben Guerir, Morocco, (3)King Abdullah University of Science and Technology, Hydrology, Agriculture and Land Observation (HALO) Laboratory, Division of Biological and Environmental Sciences and Engineering, Thuwal, Saudi Arabia, (4)King Abdullah University of Science and Technology, Center for Desert Agriculture, The Salt Lab, Thuwal, Saudi Arabia
Mangrove Growth Monitoring Using Multi-temporal UAV-LiDAR (459570)
Dameng Yin, University at Buffalo, Buffalo, NY, United States and Le Wang, University at Buffalo, Geography, Buffalo, NY, United States
Monitoring Abandoned Cropland using Dense Landsat Time Series: A Spatial-temporal Approach (450229)
Yukun Lin1, Zhe Zhu1, Shi Qiu2, Rong Shang1 and Lifu Zhang3, (1)Texas Tech University, Lubbock, TX, United States, (2)Texas Tech University, Lubbock, United States, (3)RADI Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
Planning Optimal UAS Flight Areas for Rare Plant Monitoring in the Blue Ridge Mountains of North Carolina (433892)
William Reckling, North Carolina State University Raleigh, Raleigh, NC, United States and Helena Mitasova, North Carolina State Univ, Marine, Earth, and Atmos. Sciences, Raleigh, NC, United States
Precision Farming on Large Scale Non-contiguous Circulated Lands Through a Big Data Software Platform GAGO Yunjing (458833)
Xin Peng1, Tiefeng Jiao2, Wei Gu3 and Wenpeng Zhang1, (1)Beijing GAGO Ltd., Beijing, China, (2)Agricultural Committee of Jilin Province, Information Center, Changchun，Jiling, China, (3)Zhejiang Materials Industry Chemical Group Co., Ltd., Hangzhou, Zhejiang, China
Quantifying Cotton Water Stress Using Unmanned Aerial Systems (466015)
Yazhou Sun1, Wenxuan Guo2, Zhe Zhu1, Xiaoyuan Yang3, Valeriy Kovalskyy4, Zhe Lin5 and Yukun Lin1, (1)Texas Tech University, Lubbock, TX, United States, (2)Texas Tech University, plant and soil science, Lubbock, United States, (3)The Climate Corporation San Francisco, San Francisco, CA, United States, (4)The Climate Corporation, Creve Coeur, MO, United States, (5)Texas Tech University, Lubbock, United States
Remote Detection of Water Stress over walnut orchards using Unmanned Aerial Vehicle (UAV) based Multispectral Images (385044)
Zhehan Tang1, Yufang Jin2, Ken Shackel3, Allan Fulton4 and Carolyn Haynes4, (1)University of California Davis, Davis, CA, United States, (2)University of California Davis, Department of Land, Air, and Water Resources, Davis, United States, (3)University of California, Davis, Department of Plant Sciences, Davis, United States, (4)University of California, Cooperative Extension Tehama County, Red Bluff, CA, United States
Satellite-based mapping of field-scale stress indicators for crop yield forecasting: an application across the Corn Belt, USA (397287)
Yang Yang1, Martha Anderson2, Feng Gao3, Christopher Hain4, Jason Otkin5, Brian Wardlow6, Yun Yang7, Liang Sun8, Joseph G Alfieri9 and Wayne Dulaney8, (1)USDA Beltsville Agricultural Research Center, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States, (2)USDA ARS, Hydrology and Remote Sensing Laboratory, Beltsville, United States, (3)USDA, ARS, Beltsville, United States, (4)NASA Marshall Space Flight Cetner, Huntsville, United States, (5)University of Wisconsin-Madison, CIMSS/SSEC, Madison, United States, (6)University of Nebraska-Lincoln, Lincoln, United States, (7)University of Maryland College Park, ESSIC, College Park, MD, United States, (8)USDA ARS, Beltsville, MD, United States, (9)USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD, United States
Understanding spatial variability of miscanthus yield for increased productivity on marginal lands (446844)
Paul R Adler, USDA-ARS, Pasture Systems and Watershed Management Research Unit, University Park, United States
Using Hyperspectral Imaging to Detect Tree Distress Due to Woolly Adelgid Infestation (446465)
Stacey J. Bayer1, Nefia Chacko2, Laquan Garvey2, Victoria Boos2, Carl Lipo3 and T De Smet4, (1)Binghamton University, Binghamton, NY, United States, (2)Binghamton University, Environmental Studies, Binghamton, NY, United States, (3)Binghamton University, Environmental Studies, Binghamton, United States, (4)Binghamton University, Geological Sciences and Environmental Studies, Binghamton, NY, United States
Utilising Unmanned Aerial Vehicles for mapping cropping systems: A case of Rajasthan, India (437300)
Aparna Radhakrishnan and Niti Saxena, SM Sehgal Fondation, Development Research and Policy Initatives, Gurgaon, Haryana, India

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