ReviewPlant phenomics (Washington, D.C.)2022
A Review of High-Throughput Field Phenotyping Systems: Focusing on Ground Robots.
Review in Plant phenomics (Washington, D.C.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed.
- PhenoRob-P: An autonomous robotic system for high-throughput phenotyping of potted plants.Plant phenomics (Washington, D.C.) · 2026Article
- A renewed focus on carbon uptake and use to enhance crop performance in the face of rising temperatures.Plant physiology · 2026Review
- Breeding 5.0: Artificial intelligence (AI)-decoded germplasm for accelerated crop innovation.Journal of integrative plant biology · 2026Article
- Ground Mobile Robots for High-Throughput Plant Phenotyping: A Review from the Closed-Loop Perspective of Perception, Decision, and Action.Plants (Basel, Switzerland) · 2026Review
- FIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping.Scientific data · 2026Article
- AI Agents and Agentic AI in plant breeding: new frontiers, opportunities and challenges.Frontiers in genetics · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Field-scale robotic phenotyping of three-dimensional wheat canopy architectural traits.Frontiers in plant science · 2026Article
- Maize Kernel Batch Counting System Based on YOLOv8-ByteTrack.Sensors (Basel, Switzerland) · 2025Article
- PhenoRob-F: An autonomous ground-based robot for high-throughput phenotyping of field crops.Plant phenomics (Washington, D.C.) · 2025Article
- Design and implementation of a high-throughput field phenotyping robot for acquiring multisensor data in wheat.Plant phenomics (Washington, D.C.) · 2025Article
- From ADAS to Material-Informed Inspection: Review of Hyperspectral Imaging Applications on Mobile Ground Robots.Sensors (Basel, Switzerland) · 2025Review
- Advancing Crop Resilience Through High-Throughput Phenotyping for Crop Improvement in the Face of Climate Change.Plants (Basel, Switzerland) · 2025Review
- AutoXAI4Omics: an automated explainable AI tool for omics and tabular data.Briefings in bioinformatics · 2024Article
- Review
- SCAG: A Stratified, Clustered, and Growing-Based Algorithm for Soybean Branch Angle Extraction and Ideal Plant Architecture Evaluation.Plant phenomics (Washington, D.C.) · 2024Article
- Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications.AoB PLANTS · 2023Review
- Article
- More eyes on the prize: open-source data, software and hardware for advancing plant science through collaboration.AoB PLANTS · 2023Review
- Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing.Frontiers in plant science · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Manual assessments of plant phenotypes in the field can be labor-intensive and inefficient. The high-throughput field phenotyping systems and in particular robotic systems play an important role to automate data collection and to measure novel and fine-scale phenotypic traits that were previously unattainable by humans. The main goal of this paper is to review the state-of-the-art of high-throughput field phenotyping systems with a focus on autonomous ground robotic systems. This paper first provides a brief review of nonautonomous ground phenotyping systems including tractors, manually pushed or motorized carts, gantries, and cable-driven systems. Then, a detailed review of autonomous ground phenotyping robots is provided with regard to the robot's main components, including mobile platforms, sensors, manipulators, computing units, and software. It also reviews the navigation algorithms and simulation tools developed for phenotyping robots and the applications of phenotyping robots in measuring plant phenotypic traits and collecting phenotyping datasets. At the end of the review, this paper discusses current major challenges and future research directions.
Identifiers
What OpenQuestion holds
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.