Image‐based crop disease detection using machine learning

Abstract

Abstract Crop disease detection is important due to its significant impact on agricultural productivity and global food security. Traditional disease detection methods often rely on labour‐intensive field surveys and manual inspection, which are time‐consuming and prone to human error. In recent years, the advent of imaging technologies coupled with machine learning (ML) algorithms has offered a promising solution to this problem, enabling rapid and accurate identification of crop diseases. Previous studies have demonstrated the potential of image‐based techniques in detecting various crop diseases, showcasing their ability to capture subtle visual cues indicative of pathogen infection or physiological stress. However, the field is rapidly evolving, with advancements in sensor technology, data analytics and artificial intelligence (AI) algorithms continually expanding the capabilities of these systems. This review paper consolidates the existing literature on image‐based crop disease detection using ML, providing a comprehensive overview of cutting‐edge techniques and methodologies. Synthesizing findings from diverse studies offers insights into the effectiveness of different imaging platforms, contextual data integration and the applicability of ML algorithms across various crop types and environmental conditions. The importance of this review lies in its ability to bridge the gap between research and practice, offering valuable guidance to researchers and agricultural practitioners.

Plant phenotyping relevance

植物病害を画像から検出する機械学習手法のレビューであり、感染植物の病徴・病害状態を観測画像から推定する方法が中心です。

Code and data availability

This is a review article surveying ML/AI crop disease detection methods from prior literature. The supplied blocks contain no authors' phenotype datasets, images, code, models, or supplements with explicit public availability statements; all cited datasets and models belong to prior works.

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