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Identification of BSR Disease in Oil Palm from UAV Imagery Using CNN and SCNN Approaches
Corresponding Author(s) : Zakia Azzahro
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 2, May 2026 (Article in Progress)
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.
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- 2018 International Conference On Signals And Systems : Proceedings : May 1-3, 2018, Prama Sanur Beach Hotel, Bali, Indonesia. IEEE, 2018.
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References
2018 International Conference On Signals And Systems : Proceedings : May 1-3, 2018, Prama Sanur Beach Hotel, Bali, Indonesia. IEEE, 2018.
Y. Siddiqui, A. Surendran, R. R. M. Paterson, A. Ali, And K. Ahmad, “Current Strategies And Perspectives In Detection And Control Of Basal Stem Rot Of Oil Palm,” May 01, 2021, Elsevier B.V. Doi: 10.1016/J.Sjbs.2021.02.016.
Y. Xu Et Al., “Author Correction: Recent Expansion Of Oil Palm Plantations Into Carbon-Rich Forests (Nature Sustainability, (2022), 10.1038/S41893-022-00872-1),” May 01, 2022, Nature Research. Doi: 10.1038/S41893-022-00897-6.
O. Win Kent, T. Weng Chun, T. Lee Choo, And L. Weng Kin, “Early Symptom Detection Of Basal Stem Rot Disease In Oil Palm Trees Using A Deep Learning Approach On UAV Images,” Comput Electron Agric, Vol. 213, Oct. 2023, Doi: 10.1016/J.Compag.2023.108192.
Y. H. Haw Et Al., “Classification Of Basal Stem Rot Using Deep Learning: A Review Of Digital Data Collection And Palm Disease Classification Methods,” Peerj Comput Sci, Vol. 9, 2023, Doi: 10.7717/PEERJ-CS.1325.
P. Ahmadi, S. Mansor, B. Farjad, And E. Ghaderpour, “Unmanned Aerial Vehicle (UAV)-Based Remote Sensing For Early-Stage Detection Of Ganoderma,” Remote Sens (Basel), Vol. 14, No. 5, Mar. 2022, Doi: 10.3390/Rs14051239.
M. Lestandy And A. Nugraha, “UAV Image Classification Of Oil Palm Plants Using CNN Ensemble Model,” 2025. [Online]. Available: Http://Jurnal.Polibatam.Ac.Id/Index.Php/JAIC
Y. Nuwara, W. K. Wong, And F. H. Juwono, “Modern Computer Vision For Oil Palm Tree Health Surveillance Using Yolov5,” In 2022 International Conference On Green Energy, Computing And Sustainable Technology, GECOST 2022, Institute Of Electrical And Electronics Engineers Inc., 2022, Pp. 404–409. Doi: 10.1109/GECOST55694.2022.10010668.
M. Al-Shalout And K. Mansour, “Detecting Date Palm Diseases Using Convolutional Neural Networks,” In 2021 22nd International Arab Conference On Information Technology, ACIT 2021, Institute Of Electrical And Electronics Engineers Inc., 2021. Doi: 10.1109/ACIT53391.2021.9677103.
A. Zahfran Imran, M. Aswin, And K. Ferdiana, “Identifikasi Penyakit Katarak Berdasarkan Citra Fundus Menggunakan Siamese Convolutional Neural Network”, Doi: 10.26760/Elkomika.
E. Sugiarto, F. Budiman, And A. Fahmi, “Implementation Of Deep Learning Based On Convolution Neural Network For Batik Pattern Recognition,” Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, And Control, Jan. 2025, Doi: 10.22219/Kinetik.V10i1.2019.
Y. Zhang, J. Zhang, And W. Zhou, “Research On Image Classification Improvement Based On Convolutional Neural Networks With Mixed Training,” In Proceedings Of 2022 IEEE 4th International Conference On Civil Aviation Safety And Information Technology, ICCASIT 2022, Institute Of Electrical And Electronics Engineers Inc., 2022, Pp. 7–10. Doi: 10.1109/ICCASIT55263.2022.9986643.
A. Perdananto, A. Udin Zailani, J. Kencana No, And P. Tangerang Selatan, “Penerapan Deep Learning Pada Aplikasi Prediksi Penyakit Pneumonia Berbasis Convolutional Neural Networks,” DES 2019 Journal Of Informatics And Communications Technology, Vol. 1, No. 2, Pp. 1–010.
H. Hamdani, A. Septiarini, A. Sunyoto, S. Suyanto, And F. Utaminingrum, “Detection Of Oil Palm Leaf Disease Based On Color Histogram And Supervised Classifier,” Optik (Stuttg), Vol. 245, Nov. 2021, Doi: 10.1016/J.Ijleo.2021.167753.
I. Wulandari, H. Yasin, And T. Widiharih, “KLASIFIKASI CITRA DIGITAL BUMBU DAN REMPAH DENGAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN)”, [Online]. Available: Https://Ejournal3.Undip.Ac.Id/Index.Php/Gaussian/
I. Nasution, A. Perdana Windarto, And M. Fauzan, “Penerapan Algoritma K-Means Dalam Pengelompokan Data Penduduk Miskin Menurut Provinsi,” Technology And Science (BITS, Vol. 2, No. 2, Pp. 76–83, 2020, [Online]. Available: Https://Www.Bps.Go.Id.
A. A. Nilatika, S. H. Pramono, And Rahmadwati, “Early Diagnosis Of Diabetic Retinopathy Through Optimization Of Convolutional Neural Network Hyperparameters Using Genetic Algorithm,” In ICSINTESA 2024 - 2024 4th International Conference Of Science And Information Technology In Smart Administration: The Collaboration Of Smart Technology And Good Governance For Sustainable Development Goals, Institute Of Electrical And Electronics Engineers Inc., 2024, Pp. 384–389. Doi: 10.1109/ICSINTESA62455.2024.10747887.
G. Zhao, L. Xu, X. Zhu, S. Lin, And L. Xie, “Spectrum-Matched Ground Motion Selection Method Based On Siamese Convolutional Neural Networks,” Soil Dynamics And Earthquake Engineering, Vol. 163, Dec. 2022, Doi: 10.1016/J.Soildyn.2022.107515.
M. Toby Suwindra, A. Erlansari, And J. W. Supratman Kandang Limun, “ANALISIS KEMIRIPAN JENIS BURUNG MENGGUNAKAN SIAMESE NEURAL NETWORK ANALYSIS OF BIRD SPECIES SIMILARITY USING SIAMESE NEURAL NETWORK,” 2021. [Online]. Available: Http://Ejournal.Unib.Ac.Id/Index.Php/Rekursif/193
J. Sunkpho, C. Se, W. Wipulanusat, And V. Ratanavaraha, “SHAP-Based Convolutional Neural Network Modeling For Intersection Crash Severity On Thailand’s Highways,” IATSS Research, Vol. 49, No. 1, Pp. 27–41, Apr. 2025, Doi: 10.1016/J.Iatssr.2024.12.003.
P. Narmatha, S. Gupta, T. R. Vijaya Lakshmi, And D. Manikavelan, “Skin Cancer Detection From Dermoscopic Images Using Deep Siamese Domain Adaptation Convolutional Neural Network Optimized With Honey Badger Algorithm,” Biomed Signal Process Control, Vol. 86, Sep. 2023, Doi: 10.1016/J.Bspc.2023.105264.
P. Mimboro, B. Soewito, H. Soeparno, And W. Budiharto, “Prediction Of Oil Palm Conditions Using Deep Learning Based On The Visible Atmospherically Resistant Index On UAV Imagery,” In INCITEST 2023 - Proceedings Of The 2023 International Conference On Informatics Engineering, Science And Technology, Institute Of Electrical And Electronics Engineers Inc., 2023. Doi: 10.1109/INCITEST59455.2023.10396901.