A BIBLIOMETRIC REVIEW OF DIGITAL IMAGE PROCESSING APPROACHES FOR CROP MONITORING IN PRECISION AGRICULTURE

Abstract

Monitoring of crops, is considered a crucial component of today's agriculture, which allows for early identification of pests, diseases, and stress, as well as resource optimization and the promotion of sustainable practices. Digital image processing (DIP) techniques, particularly those applied to satellite and drone photos, play an important role in soil moisture retrieval, crop health assessment, yield prediction, and insect identification. These technologies enable the deployment of precision agriculture, which leads to increased production, lower costs, and more ecologically friendly agricultural practices. Traditional manual approaches, on the other hand, are inefficient, time-consuming, and susceptible to inconsistencies, whereas DIP-based crop monitoring systems provide a more accurate, efficient, and scalable alternative. In this paper, an exhaustive review of DIP-based crop monitoring techniques using satellite and drone images, along with a bibliometric study of articles published between 2015 and 2024 and the growing impact of precision agriculture, internet of things (IoT), and machine learning on global agricultural practices, revealing key trends and influential research contributions from various regions, has been conducted. It is observed that in the future, precision agriculture systems using explainable artificial intelligence (AI) can enhance crop management by accurately identifying plant stress and infections through visualization maps, leading to smarter, data-driven farming decisions.

Plant phenotyping relevance

衛星・ドローン画像による作物状態・ストレス・感染・収量などの抽出手法を対象とするレビューであり、画像処理手法の整理が中心。ただし土壌水分や害虫同定など非フェノタイピング用途も含む。

Code and data availability

The supplied blocks contain only the conclusion, acknowledgment, and reference list of a bibliometric review. No public phenotype/trait datasets, plant images, sensor inputs, author analysis code, trained models, or supplements with such assets are described. All URLs present are citations to prior work, not paper-phen

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