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

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
Main Authors: Brandon Victor, Aiden Nibali, Saul Justin Newman, Tristan Coram, Francisco Pinto, Matthew Reynolds, Robert T. Furbank, Zhen He
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Remote Sensing
Subjects:
agriculture
deep learning
object-based image analysis
optical imagery
plant breeding
Online Access:https://www.mdpi.com/2072-4292/16/2/282

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