A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images.

Abstract

Major crops worldwide are affected by various diseases yearly, leading to crop losses in different regions. The primary methods for addressing crop disease losses include manual inspection and chemical control. However, traditional manual inspection methods are time-consuming, labor-intensive, and require specialized knowledge. The preemptive use of chemicals also poses a risk of soil pollution, which may cause irreversible damage. With the advancement of computer hardware, photographic technology, and artificial intelligence, crop disease recognition methods based on spectral and red-green-blue (RGB) images not only recognize diseases without damaging the crops but also offer high accuracy and speed of recognition, essentially solving the problems associated with manual inspection and chemical control. This paper summarizes the research on disease recognition methods based on spectral and RGB images, with the literature spanning from 2020 through early 2025. Unlike previous surveys, this paper reviews recent advances involving emerging paradigms such as State Space Models (e.g., Mamba) and Generative AI in the context of crop disease recognition. In addition, it introduces public datasets and commonly used evaluation metrics for crop disease identification. Finally, the paper discusses potential issues and solutions encountered during research, including the use of diffusion models for data augmentation. Hopefully, this survey will help readers understand the current methods and effectiveness of crop disease detection, inspiring the development of more effective methods to assist farmers in identifying crop diseases.

Plant phenotyping relevance

作物病害をRGB・スペクトル画像から認識する手法を中心に、評価指標や公開データセットを含めて体系的にレビューしており、植物の病害状態を画像から推定するフェノタイピング手法レビューに該当する。

Code and data availability

This is a survey of crop disease recognition methods. The only public assets mentioned are third-party datasets (PlantVillage, XDB, SWD, NLB, FGVC8, etc.) compiled in a review table; none are the authors' own phenotyping measurements, analysis code, models, or paper-specific data. No author code availability or deposit

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