About

About TerraSentia

TerraSentia travels between crop rows beneath the canopy, where aerial imagery cannot directly measure stems, ears and lower leaves. Four high-definition cameras record plants from several angles, while onboard compute and positioning maintain row navigation even where dense foliage degrades satellite reception.

EarthSense software converts the imagery into traits including stand count, stem width, plant and ear height, leaf-area index and soybean pod count. The robot operates for more than three hours, turns within its own footprint and handles wet field conditions. Fleets can collect repeated measurements throughout a growing season, with EarthSense reporting autonomous operation across more than 200,000 corn plots.

Capabilities

Source-backed capabilities

Navigation and obstacle avoidance
Claimed - EarthSense presents TerraSentia for autonomous under-canopy crop scouting, plant phenotyping and field data collection.
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Crop scouting
Claimed - EarthSense presents TerraSentia for autonomous under-canopy crop scouting, plant phenotyping and field data collection.
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Environmental monitoring
Claimed - EarthSense presents TerraSentia for autonomous under-canopy crop scouting, plant phenotyping and field data collection.
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Research

Data: Breaking the field phenotyping bottleneck in maize with autonomous robots
2025-01-01 - Research Publication - advancement. We demonstrate that a novel autonomous robotic platform, capable of collecting biologically relevant and commonly measured phenotypes, within a maize canopy at high-throughput, low-cost, and high-volume is now a reality. Field teams used multiple TerraSentia autonomous ground robots developed by EarthSense, Inc. (Champaign, IL) to capture data using a suite of low-cost sensors from nearly 200,000 experimental units, located at 142 unique research fields in the USA and Canada, across five years. Novel computer vision and machine learning algorithms, developed by EarthSense, Inc., analyzed these in-canopy multi-sensor data to deliver ground-truth validated plant height, ear height, stem diameter, and leaf area index at multiple time points during each season. We show the robot measured these phenotypes with high accuracy and reliability, at scales sufficient to functionally di
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TerraSentia Dataset
2022-01-01 - Research Publication - GitHub - jrcuaranv/terrasentia-dataset: This dataset is intended for the evaluation of visual-based localization and mapping systems in agriculture. · GitHub Skip to content Navigation Menu Toggle navigation Sign in Appearance settings Platform AI CODE CREATION GitHub Copilot Write better code with AI GitHub Copilot app Direct agents from issue to merge MCP Registry New Integrate external tools DEVELOPER WORKFLOWS Actions Automate any workflow Codespaces Instant dev environments Issues Plan and track work Code Review Manage code changes APPLICATION SECURITY GitHub Advanced Security Find and fix vulnerabilities Code security Secure your code as
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High precision control and deep learning-based corn stand counting algorithms for agricultural robot
2021-03-21 - Research Publication - , ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor, and stand counting, are measured manually. This is highly labor-intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator (NMHE) that identifies key terrain parameters using onboard robot sensors and a learning-based Nonlinear Model Predictive Control (NMPC) that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm designed to enable an ultra-compact ground robot to count corn stands by driving through the fields autonomously. The algorithm leverages a deep netw
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Timeline

EarthSense reports 80 TerraSentia robots deployed
2020-07-22 - Deployment - EarthSense had deployed 80 TerraSentia robots by July 2020 and intended to produce 100 before year-end. Corteva Agriscience used TerraSentia in research and development to collect plant-trait data for crop-product development. The robots used computer vision and machine learning for under-canopy measurements including vigor, ear height, soybean pods, biomass and crop stress.
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EarthSense wins NSF Phase II award for TerraSentia robotics
2020-04-13 - Award - The NSF Phase II Small Business Technology Transfer award followed EarthSense successful Phase I feasibility project. EarthSense and the University of Illinois planned further development of robotics, autonomy and edge analytics for large-scale TerraSentia deployment. The two-year grant supported final product development and global deployment after TerraSentia pilots in the United States, Brazil, Chile, Australia, France and India.
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EarthSense raises seed funding for TerraSentia
2019-06-01 - Funding - EarthSense closed a USD 1 million SAFE seed round led by Innova Memphis, with Illinois Ventures, Fox Ventures and The Syndicate Fund participating. TerraSentia was designed to collect under-canopy plant-trait data for crop breeders at lower cost than conventional field-phenotyping systems. The capital supported technical hiring, TerraSentia platform maturation and expansion beyond the agricultural research and development market.
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