High-Throughput Plot-Level Quantitative Phenotyping Using Convolutional Neural Networks on Very High-Resolution Satellite Images

Abstract

To ensure global food security, crop breeders conduct extensive trials across various locations to discover new crop varieties that grow more robustly, have higher yields, and are resilient to local stress factors. These trials consist of thousands of plots, each containing a unique crop variety monitored at intervals during the growing season, requiring considerable manual effort. In this study, we combined satellite imagery and deep learning techniques to automatically collect plot-level phenotypes from plant breeding trials in South Australia and Sonora, Mexico. We implemented two novel methods, utilising state-of-the-art computer vision architectures, to predict plot-level phenotypes: flowering, canopy cover, greenness, height, biomass, and normalised difference vegetation index (NDVI). The first approach uses a classification model to predict for just the centred plot. The second approach predicts per-pixel and then aggregates predictions to determine a value per-plot. Using a modified ResNet18 model to predict the centred plot was found to be the most effective method. These results highlight the exciting potential for improving crop trials with remote sensing and machine learning.

Plant phenotyping relevance

衛星画像と深層学習を用いて育種試験のプロット単位形質を自動推定する手法を開発・比較しており、フェノタイピング手法が研究の中心である。

Code and data availability

The supplied blocks describe purchased satellite imagery and trial measurements generously provided by AGT and CIMMYT, but contain no public data deposit, no author code/model release, and no availability statement with a public URL. The only URLs present are generic sensor-calibration references, not paper-specific,

No evidence-backed public reproduction asset is currently recorded.