ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1662972
This article is part of the Research TopicCutting-Edge Technologies Applications in Intelligent Phytoprotection: From Precision Weed and Pest Detection to Variable Fertilization TechnologiesView all 13 articles
ASSESSMENT OF THREE BROADLEAF WEED SPECIES CLASSIFICATION IN RICE FIELD USING UAV HYPERSPECTRAL IMAGING AND MACHINE LEARNING
Provisionally accepted- 1Department of Agriculture Technology, Universiti Putra Malaysia Fakulti Pertanian, Serdang, Malaysia
- 2Universiti Putra Malaysia Fakulti Pertanian, Serdang, Malaysia
- 3Department of Geography, Faculty of Arts and Social Sciences, Universiti Malaya, Federal Territory of Kuala Lumpur, Malaysia
- 4Universiti Putra Malaysia, Serdang, Malaysia
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Broadleaf weed (BLW) infestation is a major challenge in rice cultivation, particularly during the early vegetative stages when competition for resources is most critical. This study aims to enhance early-stage detection and classification of three prevalent BLW species—Monochoria vaginalis (MV), Limnocharis flava (LF), and Sphenoclea zeylanica (SZ)—in rice fields using unmanned aerial vehicle (UAV)-based hyperspectral imaging integrated with machine learning techniques. The research was conducted in a 1-hectare rice plot (Block L5A, Plot 121) near Pusat Benih Padi Felcra Sdn Bhd, Perak, Malaysia, a site characterised by high weed density. Hyperspectral data were acquired using a DJI Matrice 600 UAV equipped with a Resonon Pika L hyperspectral camera flown at 40 meters altitude. ENVI Classic 5.3 software was used to perform supervised classification based on selected regions of interest (ROIs) for training. Three classification algorithms—Support Vector Machine (SVM), Minimum Distance (MD), and Parallelepiped (PP)—were compared at 15, 25, and 30 days after sowing (DAS). Among them, SVM consistently achieved the highest classification accuracy, exceeding 99% for all weed species across all growth stages, with minimal omission and commission errors. Vegetation cover analysis showed an increasing trend in BLW expansion over time, while rice cover fluctuated and soil cover declined, indicating the competitive dominance of weeds. The findings underscore the effectiveness of UAV hyperspectral imaging combined with machine learning— especially SVM—as a scalable, accurate, and efficient approach for early weed detection. This methodology can support precision agriculture by enabling timely and targeted weed management strategies, ultimately improving rice yield and sustainability.
Keywords: hyperspectral, remote sensing, machine learning, Rice crop, weed
Received: 09 Jul 2025; Accepted: 02 Oct 2025.
Copyright: © 2025 Sulaiman, Che'Ya, Juraimi, Mohd Noor, ROSLE and MOHD ROSLIM. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Nik Norasma Binti Che'Ya, niknorasma@upm.edu.my
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