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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Systems and Technology Research
Phaklen Ehkan
Universiti Malaysia Perlis
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Assessing Palm Plant Health through Color Analysis of Leaves Using MATLAB-Based Digital Image Processing Muhammad Akbar Syahbana Pane; Khairul Saleh; Hasanal Fachri Satia Simbolon; Gerhard Wilhelm Weber; Phaklen Ehkan; Mohd Nazri Warip
Journal of Information Systems and Technology Research Vol. 4 No. 2 (2025): May 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i02.1133

Abstract

The health of oil palm plants can be visually assessed through changes in leaf color, which reflect the plant's physiological condition. Leaf color serves as a critical, non-destructive indicator for evaluating plant health. This study aims to develop an innovative method for detecting oil palm leaf health using MATLAB-based digital image processing techniques. The process begins with leaf image acquisition, followed by pre-processing to enhance image quality, and then color space conversion from RGB to HSV. The analysis focuses on the Hue and Saturation components, which represent the leaf's color tone and intensity. Two sample images—healthy and unhealthy leaves—are compared. The results demonstrate that healthy leaves exhibit higher average Hue and Saturation values compared to unhealthy ones, providing a key parameter for automated leaf condition classification. This study introduces a cost-effective system adaptable for small-scale farmers' plantations, offering an effective, efficient, and economical solution. This approach shows significant potential for implementation in automated plant health monitoring systems and further development for precision agriculture, particularly in oil palm plantations, to enhance productivity and sustainability in modern agriculture.
Performance Evaluation of YOLOv9, YOLOv10, and YOLOv11 for Real-Time Early Detection of Ganoderma Boninense in Oil Palm Rizky Delianngi; Ratu Mutiara Siregar; Nurliana; Muhammad Akbar Syahbana Pane; Phaklen Ehkan; Andi Prayogi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.7479

Abstract

Early detection of Ganoderma boninense infection is essential to reduce yield losses in oil palm plantations. This study aims to evaluate the performance of three recent YOLO architectures, namely YOLOv9, YOLOv10, and YOLOv11, for real-time detection of early infection symptoms under natural field conditions. A dataset of 2,000 annotated RGB images was used with a 70:20:10 split for training, validation, and testing. Model performance was evaluated using precision, recall, F1-score, mean average precision (mAP50 and mAP50–95), and inference speed. The results show that YOLOv9 achieved the highest detection accuracy with an mAP50 of 0.989 and F1-score of 0.968. Meanwhile, YOLOv11 demonstrated the best computational efficiency with an inference speed of 35 FPS and processing time of 28.5 ms per frame. These findings indicate a trade-off between accuracy and speed, where YOLOv9 is suitable for accuracy-oriented applications, while YOLOv11 is more appropriate for real-time deployment in precision agriculture.
Co-Authors Andi Prayogi Gerhard Wilhelm Weber Hasanal Fachri Satia Simbolon Khairul Saleh Mohd Nazri Warip Muhammad Akbar Syahbana Pane Muhammad Akbar Syahbana Pane Nurliana Ratu Mutiara Siregar Rizky Delianngi