Plant Disease Detection Using Digital Image Processing: Opportunities and Challenges

Abstract

Diseases in plants affect the yield of the plant itself. Agriculture is essential in human life, and if plant conditions are left unchecked, it will result in crop failure, which can affect the economy. Many researchers have developed methods to detect plant diseases, ranging from expert systems to deep learning algorithms. Machine learning is particularly effective for this task as it relies on datasets composed of plant images, making image processing crucial for the identification process. This article reviews the current literature and identifies several research gaps, opportunities, and challenges that must be addressed. Specifically, the article outlines potential avenues for future research in detecting plant diseases using image processing techniques. A significant opportunity exists to develop more effective algorithmic models for detecting plant diseases.

Plant phenotyping relevance

植物病害を画像処理で検出する手法の文献レビューであり、植物の病害状態を観測・推定するフェノタイピング手法が中心です。

Code and data availability

This is a literature review of plant disease detection methods; it presents no original phenotyping measurements, datasets, images, code, or models of its own. All datasets and methods discussed (e.g., PlantVillage) belong to cited prior work, and no author code or data availability statement appears in the supplied.

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