A Detailed Review on Plant Leaf Disease Detection and Classification Methodologies using Deep Learning Techniques

Abstract

The rapid emergence and evolution of deep learning methodologies in the field of plant disease classification and detection has resulted in significant progress. Their application has revolutionized the way agriculture is done. This paper provides an overview of the advancements in utilizing deep learning models to address the crucial task of identifying and categorizing plant diseases. By harnessing the power of deep convolutional neural networks (CNNs) and transfer learning, researchers have achieved remarkable accuracy in disease classification, often surpassing traditional methods. This study also delves into the challenges that persist in this field, such as the scarcity of labeled data and potential biases in models. To address these concerns, the integration of visualization techniques is explored, allowing for better model interpretation and transparency. The collaborative efforts of agricultural experts and machine learning researchers are deemed crucial for overcoming these challenges and driving the future direction of research. Looking ahead, the interdisciplinary approach is anticipated to play a pivotal role in refining deep learning models for plant disease detection. A seamless collaboration between domain-specific professionals, machine learning experts, and agricultural practitioners is essential to foster innovation, enhance the reliability of models, and create a sustainable agricultural ecosystem. With the integration of cutting-edge architectures, emerging technologies like edge computing, and broader datasets, the field is poised to bring about transformative changes in agricultural practices, bolstering crop health and productivity.

Plant phenotyping relevance

植物病害を画像等から検出・分類する深層学習手法を主題としたレビューであり、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に該当する。

Code and data availability

This is a review article on deep learning for plant leaf disease detection. It surveys publicly available datasets (PlantVillage, Tomato Leaf Disease, Cassava, etc.) and CNN architectures, but these are cited prior-work resources, not assets produced by this paper. The article contains no original phenotype/trait data,

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