A review on machine learning and deep learning techniques for plant leaf disease detection and classification with IoT in agriculture industry

Abstract

Agriculture serves as a major source of food and plays a key function as the backbone of most countries’ economies. However, farmers are encountering many challenges in this sector, such as drought, flooding, diseases, nutrient deficiency, and so on. The technological advancements in the field of agriculture, also called smart agriculture, are necessary to address the requirements of the expanding population and manage the associated challenges. Among those, plant leaf diseases are the primary concern that severely impacts crop yield and economic stability. This technical review examines various Machine Learning (ML) and Deep Learning (DL) approaches used to identify and classify different plant leaf diseases. This review gives an overview of the current state-of-the-art ML, DL, and IoT-enabled disease prediction systems and their recent advances in developing an intelligent system in smart agriculture. It provides insights into the various technological developments and discusses the benefits and opportunities of AI-based models in plant disease management.

Plant phenotyping relevance

植物葉の病徴を画像・機械学習で検出・分類する手法を中心に扱うレビューであり、植物の疾病状態を推定するフェノタイピング手法レビューに該当する。

Code and data availability

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