Plant leaf disease detection and classification using artificial intelligence techniques: a review

Abstract

Agriculture is a cornerstone of human civilization, providing both food and economic stability. While not necessarily fatal, leaf diseases are a crucial threat to plant health. Accurate detection and classification of diseases in early stages are essential to minimize damage. Manual identification can be challenging, and delays in detection can lead to crop devastation. Fortunately, computer-aided image processing offers a solution. Researchers have explored several techniques for disease detection and classification by usage of affected leaf images, making significant progress over time. However, there's always room for improvement. Machine learning (ML), Deep learning (DL) techniques have shown hopeful results. ML, DL approaches act as black-box; eXplainable AI (XAI) provides clear explanations on decisions made by these black-boxes. This study aims to present a comprehensive review on plant leaf disease detection and classification by means of ML, DL and XAI methods with an overview of the outcomes of existing techniques, summarizes their performance, evaluation metrics, and analyses the challenges in existing systems, and offers the study's inferences.

Plant phenotyping relevance

植物葉の病害を画像から検出・分類する手法について、ML・DL・XAIの性能や評価指標を体系的にレビューしており、植物状態の画像ベース推定が中心である。

Code and data availability

This is a review article surveying ML/DL/XAI plant leaf disease detection. It reports no authors' own phenotyping measurements, image collections, analysis code, or trained models. Table 2 lists publicly available datasets (PlantVillage, Plant Pathology 2021, etc.), but these are cited prior-work resources, not paper-­

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