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

TL;DR

This paper reviews recent methods for detecting crop diseases using spectral and RGB images, highlighting new AI techniques and data tools to help farmers.

Contribution

The paper introduces a survey of recent advances in crop disease recognition, including State Space Models and Generative AI.

Findings

Spectral and RGB image-based methods offer non-destructive, accurate, and fast crop disease detection.

Emerging AI paradigms like Mamba and Generative AI are being applied to improve disease recognition.

Diffusion models are proposed for data augmentation to address research challenges.

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.

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