EarthSense Inc is creating dramatic new possibilities for crop breeders, plant protection products developers, crop scientists, and field agronomists. Their first robot—TerraSentia—improves the quantity, accuracy, cost and speed of in-field plant trait data collection, especially for under-canopy traits that cannot be obtained by other technologies. TerraSentia was developed at the University of Illinois at Urbana-Champaign with support from ARPA-E within the US Department of Energy.
TerraSentia's machine vision and machine learning based analytics seamlessly convert field data to specific, actionable information about plant-traits. Following a successful 2019 field season, EarthSense has improved TerraSentia hardware, software, and analytics based on pioneering users' experience. A cloud-based platform will allow users to easily teach TerraSentia to automatically measure a variety of key traits. For more information see the IDTechEx report on Agricultural Robots and Drones 2018-2038: Technologies, Markets and Players.
TerraSentia uses a variety of sensors—including visual cameras, LIDAR and other on-board sensors—to autonomously collect data on traits for plant health, physiology, and stress response. TerraSentia's unique dataset delivers high-value under-canopy plant traits including stand-count, stem width, plant height, LAI, etc.
TerraSentia has been deployed in corn, soybean, wheat, sorghum, vegetable crops, orchards and vineyards. In 2019, EarthSense worked with leading private- and public-sector organizations to accurately detect and quantify high-value traits in corn, soybean, wheat, sorghum, etc.
TerraSentia is now being taught to measure early vigor, corn ear height, soybean pods, plant biomass and to detect and identify diseases and abiotic stresses.
Source and top image: EarthSense Inc, TerraSentia