REVIEW OF RESEARCH ON RECOGNITION AND MONITORING OF PLANT GROWTH PHENOTYPE BASED ON DEEP LEARNING

Abstract

Accurate measurement of plant phenotypic data can provide a comprehensive understanding of plant physiology and help to study the relationship between plant genes and the environment. The application of visible light and other multi-source and multi-dimensional imaging sensing technology can provide a rich data source for plant phenotype identification and monitoring. With the continuous development and application of computer technology in the field of plant phenotype analysis, deep learning technology has made remarkable achievements in plant phenotype identification and monitoring. On the basis of reviewing the relevant research results at home and abroad at this stage,this paper firstly describes the common ways of plant phenotype image acquisition; then it discusses in detail the current status of the application of deep learning technology in the fields of classification, detection and segmentation of plant phenotypes, crop development and yield prediction, as well as plant drought and pest stress, etc.; and finally it discusses the challenges and future development goals of the deep learning method in the monitoring and recognition of plant phenotypes.This paper aims to provide theoretical support and technical reference for the development and application of deep learning technology in the field of agricultural plant phenotyping.

Plant phenotyping relevance

植物フェノタイピングにおける画像取得と深層学習による分類・検出・セグメンテーション、成長・収量予測などを体系的にレビューしており、方法論が中心です。

Code and data availability

This is a review article with no authors' own phenotyping measurements, datasets, images, models, or analysis code. The only public resource mentioned (IDADP dataset) is a cited third-party dataset in the references, not a paper-specific asset reproducing this paper's analysis.

No evidence-backed public reproduction asset is currently recorded.