ArticleCommunications biology2025
Breaking the field phenotyping bottleneck in maize with autonomous robots.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Harnessing high-throughput phenotyping and artificial intelligence for soybean breeding: From trait assessment to data-driven decisions.Plant phenomics (Washington, D.C.) · 2026Review
- PhenoRob-P: An autonomous robotic system for high-throughput phenotyping of potted plants.Plant phenomics (Washington, D.C.) · 2026Article
- Omics-driven plant breeding through phenomics-enviromics crosstalk.Nature communications · 2026Review
- Ground Mobile Robots for High-Throughput Plant Phenotyping: A Review from the Closed-Loop Perspective of Perception, Decision, and Action.Plants (Basel, Switzerland) · 2026Review
- OpenEar: an ultra-affordable, high-throughput, and accurate maize ear phenotyping system.Plant methods · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Beyond the genome: the role of functional markers in contemporary plant breeding.Frontiers in plant science · 2025Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Understanding phenotypic plasticity in maize (Zea mays L.) is a current grand challenge for continued crop improvement. Measuring the interactive effects of genetics, environmental factors, and management practices (GxExM) on crop performance is time-consuming, expensive, and a major bottleneck to yield advancement. We demonstrate that an 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 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. 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. The robot measured these phenotypes with high accuracy and reliability, at scales sufficient to dissect interactions between genotypes and nitrogen rates in several environments. The results show that within-row, autonomous field robots hold great promise to increase GxExM understanding and decrease the amount of human labor required for plant phenotyping.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.