A Survey on Deep Learning Approaches for Crop Disease Analysis in Precision Agriculture

Abstract

Precision agriculture has emerged as a transformative paradigm in modern farming, leveraging advanced technologies to optimize crop management. This paper presents a comprehensive survey of deep learning approaches for crop disease analysis in precision agriculture. The investigation focuses on four key aspects: leaf disease detection through deep learning techniques, leaf shape-based disease analysis, crop weed detection utilizing deep learning methods, and crop damage detection using aerial images. The survey encompasses a review of recent advancements, methodologies, challenges, and future prospects in each of these domains. By exploring the intersection of deep learning and precision agriculture, this paper aims to provide a holistic understanding of the current state-of-the-art and inspire further research initiatives to enhance crop health monitoring and management.

Plant phenotyping relevance

作物病害を画像から検出・解析する深層学習手法を体系的にレビューしており、植物の病害状態を推定するフェノタイピング手法のレビューが中心である。

Code and data availability

公開論文であることは確認できましたが、現在の公式API・許可済み取得経路では本文を自動取得できませんでした。

No evidence-backed public reproduction asset is currently recorded.