Uncrewed Ground Vehicles Enable Effective Monitoring of Crop Conditions
Outcome/Accomplishment
Monitoring crop health is essential for successful production, but it has been a labor-intensive process that can impede other important operations on the farm. Over the past few years, researchers have developed a system that monitors important crop characteristics using uncrewed ground vehicles (UGVs) and a system of sensors. This work is supported by the National Science Foundation (NSF)-funded Internet of Things for Precision Agriculture (NSF IoT4Ag) Engineering Research Center (ERC), which is headquartered at the University of Pennsylvania.
Impact/Benefits
Precision farming relies on automation and technology that provide timely data for better decision-making. The NSF IoT4Ag system overcomes the challenges of existing robotic platforms: those designed to move between rows often cannot navigate under plant canopies, while larger systems that travel above crops can be too heavy, frequently leading to soil compaction. The NSF IoT4Ag system enables in-row and under-canopy navigation in corn fields, collects physical samples, and integrates a suite of sensors to measure soil and plant conditions—allowing farmers to take corrective measures more quickly and effectively.
Explanation/Background
Timely and dense crop scouting is critical for targeted in-field interventions, but current methods are labor-intensive, less likely to adequately characterize actual field variability, often fail to capture the full range of differences within a field, and can interfere with other farm operations. The NSF IoT4Ag system employs UGVs outfitted with systems that use LiDAR (Light Detection and Ranging) technology to simultaneously locate and map corn fields, model predictive control, and receive data from soil and plant tissue sampling. These capabilities provide a scalable approach to monitoring and managing crop health.
Location
Philadelphia, Pennsylvaniawebsite
Start Year
Microelectronics and IT
Quantum, Microelectronics, Sensing, and IT
Lead Institution
Core Partners
Fact Sheet
Outcome/Accomplishment
Monitoring crop health is essential for successful production, but it has been a labor-intensive process that can impede other important operations on the farm. Over the past few years, researchers have developed a system that monitors important crop characteristics using uncrewed ground vehicles (UGVs) and a system of sensors. This work is supported by the National Science Foundation (NSF)-funded Internet of Things for Precision Agriculture (NSF IoT4Ag) Engineering Research Center (ERC), which is headquartered at the University of Pennsylvania.
Location
Philadelphia, Pennsylvaniawebsite
Start Year
Microelectronics and IT
Quantum, Microelectronics, Sensing, and IT
Lead Institution
Core Partners
Fact Sheet
Impact/benefits
Precision farming relies on automation and technology that provide timely data for better decision-making. The NSF IoT4Ag system overcomes the challenges of existing robotic platforms: those designed to move between rows often cannot navigate under plant canopies, while larger systems that travel above crops can be too heavy, frequently leading to soil compaction. The NSF IoT4Ag system enables in-row and under-canopy navigation in corn fields, collects physical samples, and integrates a suite of sensors to measure soil and plant conditions—allowing farmers to take corrective measures more quickly and effectively.
Explanation/Background
Timely and dense crop scouting is critical for targeted in-field interventions, but current methods are labor-intensive, less likely to adequately characterize actual field variability, often fail to capture the full range of differences within a field, and can interfere with other farm operations. The NSF IoT4Ag system employs UGVs outfitted with systems that use LiDAR (Light Detection and Ranging) technology to simultaneously locate and map corn fields, model predictive control, and receive data from soil and plant tissue sampling. These capabilities provide a scalable approach to monitoring and managing crop health.