Apple Plant Disease Classification: Methods, Technologies, and Future Trends

Abstract

Apple production, a cornerstone of global agriculture, faces significant threats from diseases such as apple scab, fire blight, powdery mildew, and cedar apple rust, which reduce yield, quality, and sustainability. Early and accurate disease classification is essential to mitigate economic losses and ensure food security. This paper evaluates traditional and modern approaches to apple plant disease classification, including manual visual diagnosis, image-based techniques, and molecular methods like PCR and ELISA. While traditional methods are accessible but error-prone, advanced technologies such as machine learning, deep learning, and sensor-based systems offer high accuracy and scalability, achieving up to 95% detection rates in controlled settings. Challenges, including limited labeled datasets, high computational costs, and poor model generalization across apple varieties and regions, hinder widespread adoption. Emerging trends, such as generative AI, explainable AI, drone-based monitoring, and edge computing, promise to enhance real-time diagnostics and accessibility. The paper also explores opportunities for integrating these technologies with precision agriculture to optimize orchard management and promote sustainability. By synthesizing current methods, technologies, and research gaps, this paper provides a comprehensive roadmap for researchers, farmers, and policymakers to advance apple disease management, fostering sustainable agricultural practices and global food security.

Plant phenotyping relevance

リンゴ植物の病徴・病害状態を画像、センサー、機械学習等で分類する方法を主題としたレビューであり、植物フェノタイピング手法の総説に該当する。

Code and data availability

This is a narrative review of apple disease classification methods with no original phenotyping measurements, datasets, images, code, or models of its own. The only public URL mentioned (Cornell Fire Blight Fact Sheet) is a cited external reference, not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.