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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Azzahro, Zakia
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Identification of BSR Disease in Oil Palm from UAV Imagery Using CNN and SCNN Approaches Azzahro, Zakia; Rahmadwati; Angger Abdul Razak; Amrul Faruq
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2546

Abstract

Basal Stem Rot (BSR) disease caused by Ganoderma boninense is a major threat to oil palm productivity due to its destructive nature and the challenges associated with early-stage detection. To support sustainable production and mitigate significant yield losses, a system capable of identifying tree conditions into healthy and infected categories is required. In this study, two deep learning approaches, CNN and SCNN, are applied to identify oil palm conditions based on UAV-derived imagery. While CNN is widely used for image-based detection tasks due to its ability to extract relevant visual representations, it is prone to overfitting during training, therefore SCNN is employed to address this issue by leveraging image similarity comparison. Experimental results show that both methods achieve high accuracy, with SCNN outperforming CNN by achieving an accuracy of 96.48%, compared to 95.644%. The superior performance of SCNN demonstrates its sensitivity to subtle visual differences between healthy and early-stage infected trees, enabling more reliable models. Thus, SCNN is considered more optimal for detection oil palm conditions and contributes to reducing overfitting, resulting in improved model stability.
Co-Authors Amrul Faruq Angger Abdul Razak Rahmadwati