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Deep learning in precision phytopathology: a comprehensive survey of CNN architectures for disease detection and severity quantification
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DOI：10.1007/s10462-026-11659-7.png)
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Abstract
En 中文
Plant disease detection and severity estimation are crucial to sustainable agricultural productivity and global food security, necessitating the need for efficient and accurate diagnostic tools. This paper systematically analyzes 137 studies using the PRISMA 2020 framework, focusing on deep learning methods used in detecting and estimating plant disease severity. The review covers classification, detection, segmentation, and regression approaches to diagnosing plant diseases and quantifying severity, detailing the transition in architecture design from traditional CNN to hybrid CNN-transformer frameworks. The findings show that the best precision in severity estimation is achieved by using a segmentation approach, while regression strikes a good balance between accuracy and performance, whereas classification stands out as the most scalable model. However, there is still a performance drop from laboratory to the field settings due to the problems of domain shift, class imbalance, and long-tail distributions. This paper highlights domain adaptation, few-shot learning, and multimodal fusion as the key techniques to achieve better generalizability. Robotic implementation and explainable AI are also discussed as crucial aspects in implementing practical disease diagnostic systems. This work provides a system-level synthesis to guide the development of robust, interpretable, and field-ready plant disease diagnostic systems.
Keywords:
Convolutional neural networks
Precision agriculture
Plant disease detection
Severity quantification
Computer vision
Deep Learning
Systematic literature review
Domain shift
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