中国农业科技导报2026，Vol.28Issue(8)：114-124,11.DOI:10.13304/j.nykjdb.2025.0855
基于ResNet50的端到端深度学习模型在玉米干旱胁迫多光谱监测中的应用
Multispectral Monitoring of Drought Stress in Maize Using ResNet50-based End-to-end Deep Learning Model
摘要
Abstract
With the increasing severity of drought threats due to global climate change,developing accurate and efficient crop stress monitoring methods is crucial for ensuring food security.The performance of end-to-end deep learning and traditional machine learning models in classifying drought stress levels in maize was evaluated and compared.Six-channel multispectral images of 8 maize inbred lines under 5 different water treatments were collected using a high-throughput phenotyping platform.Based on this data,an end-to-end deep learning model with a ResNet50 architecture and a random forest machine learning model built on 48 manually extracted phenotypic features were constructed and compared.The results demonstrated that the ResNet50 model achieved superior classification performance,with a macro-average F1-score of 0.887,significantly outperforming other benchmark models.Interpretability analysis via channel masking further revealed that the exceptional performance of ResNet50 primarily stemmed from its ability to autonomously learn complex stress-related patterns from the red and near-infrared spectral bands,which extended beyond the limits of human visual perception.In contrast,the random forest model relied mainly on morphological parameters and the chlorophyll carotenoid index.To sum up,the end-to-end deep learning models possessed distinct advantages over traditional feature engineering methods for crop stress grading using high-dimensional multispectral data,providing reliable technical pathway for the development of intelligent precision irrigation and crop health monitoring systems.关键词
干旱胁迫/深度学习/多光谱成像/高通量表型/玉米Key words
drought stress/deep learning/multispectral imaging/high-throughput phenotyping/maize分类
农业科技引用本文复制引用
宋键,张美琦,刘成,张春义,王维轩..基于ResNet50的端到端深度学习模型在玉米干旱胁迫多光谱监测中的应用[J].中国农业科技导报,2026,28(8):114-124,11.基金项目
新一代人工智能国家科技重大专项(2022ZD0115701) （2022ZD0115701）
三亚中国农业科学院国家南繁研究院南繁专项(YBXM2444) （YBXM2444）
中国农业科学院科技创新工程项目(CAAS-ZDRW202004). （CAAS-ZDRW202004）
