In-Field Crop Health Monitoring Using Intelligent Image Processing over Internet of Things Framework: A Comprehensive Review

Abstract

Agricultural industry endeavors to increase the productivity and quality of crops at reduced costs, effort, and time. An obvious requirement is extensive crop health monitoring, which includes early detection of crop diseases and treatment, detection of intruders like birds, animals, and humans in the farms and their repulsion, and assessment of crop water requirements and irrigation. Unlike traditional agri-practices, advanced digital frameworks, such as Artificial Intelligence, Computer Vision, Edge Computing, and Internet of Things, provide much promising solutions, thereby escalating exhaustive researches in the agricultural domain. This communication reviews key crop health monitoring systems developed over the past decade, outlining their advantages and limitations, shedding light on real-world implementation challenges, and proposing potential directions for future research.

Plant phenotyping relevance

画像処理・AIを用いた作物の健康状態や病害の検出システムをレビューしており、植物状態の取得・判定手法が中心的に扱われている。ただし侵入者検知や灌漑需要評価も含むため、植物表現型への焦点はやや広い。

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