A systematic review of deep learning techniques for apple leaf diseases classification and detection.

Abstract

Agriculture sustains populations and provides livelihoods, contributing to socioeconomic growth. Apples are one of the most popular fruits and contains various antioxidants that reduce the risk of chronic diseases. Additionally, they are low in calories, making them a healthy snack option for all ages. However, several factors can adversely affect apple production. These issues include diseases that drastically lower yield and quality and cause farmers to lose millions of dollars. To minimize yield loss and economic effects, it is essential to diagnose apple leaf diseases accurately and promptly. This allows targeted pesticide and insecticide use. However, farmers find it difficult to distinguish between different apple leaf diseases since their symptoms are quite similar. Computer vision applications have become an effective tool in recent years for handling these issues. They can provide accurate disease detection and classification through massive image datasets. This research analyzes and evaluates datasets, deep learning methods and frameworks built for apple leaf disease detection and classification. A systematic analysis of 45 articles published between 2016 and 2024 was conducted to evaluate the latest developments, approaches, and research needs in this area.

Plant phenotyping relevance

リンゴ葉の病徴を画像から検出・分類する深層学習手法を体系的に評価したレビューであり、植物の病害状態を観測するフェノタイピング手法が中心です。

Code and data availability

This is a systematic review of prior literature; the datasets mentioned (PlantVillage, Kaggle Plant Pathology) belong to cited third-party studies, not to this paper's own phenotyping measurements or analysis. The Data Availability Statement confirms no paper-specific data or code exists.

No evidence-backed public reproduction asset is currently recorded.