蔬菜Issue(8)：1-12,12.DOI:10.26924/j.cnki.sczz.2026.8001
深度学习在蔬菜生长监测中的应用研究进展
Research Progress on the Application of Deep Learning in Vegetable Growth Monitoring
摘要
Abstract
To comprehensively understand the application progress of deep learning technology in vegetable growth monitoring,this paper systematically reviews and analyzes two major research directions:monitoring techniques based on computer vision models and those based on temporal and multimodal data,based on extensive literature research.Studies have shown that deep learning technologies,particularly convolutional neural networks(CNN),recurrent neural networks(RNN),Transformers model,and multimodal deep learning methods combining image and sensor data,have demonstrated outstanding performance in non-destructive estimation of vegetable growth indicators,pest and disease identification,yield prediction and physiological index detection,significantly enhancing the automation and precision of monitoring.However,challenges such as significant fluctuations in data quality,high annotation costs,poor model transferability,limited computational resources on edge devices and insufficient model generalization capabilities remain.To address these issues,strategies such as multi-source data fusion,weakly supervised learning,cross-domain model deployment,model lightweighting and edge-cloud collaboration,as well as multi-task learning have been proposed.In the future,with advancements in automated data acquisition,cross-modal learning,and intelligent regulation technologies,deep learning is expected to play an even more critical role in vegetable growth monitoring and smart agricultural management.关键词
深度学习/蔬菜/生长监测/计算机视觉/时间序列预测/多模态融合/卷积神经网络/Transformer模型Key words
deep learning/vegetable/growth monitoring/computer vision/time series prediction/multimodal fusion/convolutional neural network(CNN)/Transformer model分类
农业科技引用本文复制引用
胡月,缪祎晟,吴华瑞..深度学习在蔬菜生长监测中的应用研究进展[J].蔬菜,2026,(8):1-12,12.基金项目
国家重点研发计划项目(2024YFD2000805) （2024YFD2000805）
国家现代农业产业技术体系项目(CARS-21-D07) （CARS-21-D07）
北京市智慧农业创新团队项目(BAIC10-2025). （BAIC10-2025）
