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Related Experiment Video

Updated: Jul 2, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

Contrastive multi-view representation learning for multi-camera plant phenotyping: A cotton field study.

Daniel Petti¹, Changying Li¹, Ninghao Liu²

¹Bio-Sensing, Automation, and Intelligence Laboratory, Department of Agricultural and Biological Engineering, Institute of Food and Agricultural Sciences, University of Florida, 1741 Museum Rd, Gainesville, 32603, Florida, USA.

Plant Phenomics (Washington, D.C.)

|July 1, 2026

Summary

Self-Supervised Learning (SSL) with multiple camera views significantly improves cotton boll detection accuracy. This approach enhances agricultural computer vision by reducing the need for extensive data annotation.

Keywords:

Contrastive learning Cotton yield estimation High-throughput phenotyping Machine vision Self-supervised learning UGV

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Last Updated: Jul 2, 2026

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

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Published on: March 28, 2025

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot

Published on: January 9, 2026

Area of Science:

Agricultural Science
Computer Vision
Machine Learning

Background:

Computer vision in agriculture is hindered by limited annotated data.
Self-Supervised Learning (SSL) offers a solution by using automatically generated annotations for model pre-training.

Purpose of the Study:

To utilize a multi-camera cotton boll dataset for contrastive learning.
To enable phenotyping tasks with minimal data annotation using SSL frameworks like SimCLR and MoCo.
To investigate the impact of camera positions on SSL performance.

Main Methods:

Collected a dataset of cotton boll images from six field camera views.
Applied contrastive learning frameworks (SimCLR, MoCo) using multi-camera positive examples.
Conducted linear evaluation and semi-supervised learning on boll detection and plot status tasks.
Analyzed the effect of camera poses and data overlap using synthetic data.

Main Results:

Multi-camera SSL improved cotton boll detection mean average precision by 14% compared to single-view methods.
Intermediate camera pose overlap showed better performance.
SSL representations captured meaningful plant features (e.g., boll density) and less meaningful ones (e.g., lighting).
Neither SimCLR nor MoCo was consistently superior.

Conclusions:

Multi-camera contrastive learning effectively enhances agricultural computer vision tasks with limited data.
Camera configuration is crucial for optimal performance in field-based SSL.
This technique can accelerate the development of robotic phenotyping algorithms.