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All Journal Smart Techno (Smart Technology, Informatic and Technopreneurship) International Journal of Technology and Modeling
Fatimah, Aisya
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Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Journal : International Journal of Technology and Modeling

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Unmanned Aerial Vehicles (UAVs) for Pest and Disease Detection in Rice Cultivation: A Systematic Review Saluky, Saluky; Marine, Yoni; Fatimah, Aisya
International Journal of Technology and Modeling Vol. 4 No. 3 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i3.158

Abstract

This paper presents a systematic review of the use of Unmanned Aerial Vehicles (UAVs) for pest and disease detection in rice cultivation, a critical challenge in maintaining yield stability and reducing chemical overuse in global food systems. The study aims to synthesize current approaches, technologies, and algorithms employed in UAV-based monitoring of rice pests and diseases, while identifying research gaps and future directions for precision rice farming. Following PRISMA-inspired guidelines, a Systematic Literature Review (SLR) was conducted across major scientific databases (Scopus, Web of Science, IEEE Xplore, and ScienceDirect) using predefined keyword combinations related to UAVs, rice, pest/disease detection, and remote sensing. Inclusion criteria focused on peer-reviewed studies that explicitly employed aerial platforms for detecting biotic stress in rice, while review papers, non-rice crops, and purely simulation-based works were excluded. The findings highlight three dominant technology dimensions: sensing modalities, with RGB and multispectral imagery being most prevalent, followed by hyperspectral and thermal sensors; analytical methods, ranging from traditional vegetation indices and thresholding to advanced machine learning and deep learning models; and operational considerations, including flight altitude, spatial resolution, and temporal frequency of data acquisition. The review contributes by proposing a conceptual framework linking sensor choice, image processing pipelines, and pest/disease symptom characteristics in rice, and by outlining open challenges regarding data standardization, smallholder adoption, and model transferability across regions.
Co-Authors Marine, Yoni Saluky