Comparative Review of Modern Deep Learning Techniques for Intelligent Plant Disease Detection

Abstract

Abstract— Plant diseases significantly affect agricultural productivity, food quality, and global food security [1], [16] Traditional disease diagnosis methods rely heavily on human expertise and manual inspection, making the process time-consuming, expensive, and prone to errors [1], [20]. Recent advancements in artificial intelligence and deep learning have transformed plant disease detection systems by enabling automatic, accurate, and real-time identification of plant diseases using digital images [2], [3], [20]. This review paper presents a comprehensive analysis of recent deep learning approaches used for automated plant disease detection. The study discusses various convolutional neural network architectures, transfer learning methods, attention mechanisms, Vision Transformers, and hybrid deep learning techniques applied in agricultural disease diagnosis [2]– [5], Publicly available datasets, evaluation metrics, preprocessing techniques, and comparative analyses of existing methods are also presented. Furthermore, the paper identifies current research challenges such as dataset imbalance, environmental variability, computational complexity, and limited real-world adaptability[16]. Finally, emerging trends including explainable artificial intelligence, federated learning, lightweight edge computing models, drone-based monitoring systems, and multimodal agricultural intelligence are explored [6]. This review aims to provide researchers and practitioners with a detailed understanding of the current state-of-the-art deep learning techniques for intelligent plant disease detection and future research opportunities. Keywords: Plant Disease Detection, Deep Learning, Convolutional Neural Network, Transfer Learning, Precision Agriculture, Computer Vision, Smart Farming, Vision Transformer.

Plant phenotyping relevance

植物病害を画像から自動検出する深層学習手法を体系的に比較・レビューしており、植物状態の画像ベース表現型計測が中心である。

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