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

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 mon...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-01, Vol.16 (2), p.282
Main Authors: Victor, Brandon, Nibali, Aiden, Newman, Saul Justin, Coram, Tristan, Pinto, Francisco, Reynolds, Matthew, Furbank, Robert T, He, Zhen
Format: Article
Language:English
Subjects:
Agricultural production
agriculture
Artificial neural networks
Classification
Comparative analysis
Computer vision
Crops
Data collection
Deep learning
Flowering
Food security
Food supply
Genotype & phenotype
Growing season
Identification and classification
Image resolution
Machine learning
Machine vision
Measurement
Medical imaging equipment
Neural networks
Normalized difference vegetative index
object-based image analysis
optical imagery
Phenotypes
Phenotyping
Physiology
Plant breeding
Remote sensing
Satellite imagery
Satellite imaging
Security management
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16020282