Detecting Monkey Pests Using Convolutional Neural Networks
DOI:
https://doi.org/10.64803/cessmuds.v1.15Keywords:
neural network convolution, monkey pests, coffee plants, classificationAbstract
Coffee is one of the agricultural products that has a fairly high value, making it one of the export commodities. Ironically, however, the amount of exports from 2021 to 2024 has continued to decline, partly due to monkey infestations. Efforts to prevent monkey attacks have been made by designing monkey detection based on PIR-HC-SR04 sensors, ultrasonic sensors, and combined with a microcontroller as a controller, but the results have not been able to distinguish between monkeys, cows, and humans passing through the plantation area. For this reason, the use of cameras will be tried as a means of sensing the plantation area and replacing the use of old sensors. Therefore, the purpose of this study is to optimize the detection of monkey pests based on digital images. To achieve this research objective, the convolution neural network (CNN) method is used. In order to obtain maximum results, the CNN will be trained beforehand so that it can correctly distinguish between monkeys, cows, humans, and so on. The results obtained are not yet optimal due to the occurrence of monkey pest attacks.
References
Arshad, U. (2021). Object Detection in Last Decade - A Survey. Scientific Journal of Informatics, 8(1), 60–70. https://doi.org/10.15294/sji.v8i1.28956
Convolutional Neural Network. (n.d.). The MathWorks, Inc. https://www.mathworks.com/discovery/convolutional-neural-network-matlab.html
Dewi, R. N. H., Ariyani, A. M., Widodo, R. C., Miharjo, E. S. R., Mutohhar, A., & Nursyahidah, F. (2023). Pencegahan Hama Kera sebagai Upaya Mengatasi Permasalahan Petani Alpukat Desa Sumberahayu. Jurnal Pemberdayaan Masyarakat, 2(2), 80–88. https://doi.org/10.46843/jmp.v2i2.287
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010). Object Detection with Discriminatively Trained Part Based Models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627–1645. https://doi.org/10.1109/TPAMI.2009.167
Felzenszwalb, P., Girshick, R., McAllester, D., & Ramanan, D. (2013). Visual Object Detection With Deformable Part Models. Communications of the ACM, 56(9), 97–105. https://doi.org/10.1145/2494532
Felzenszwalb, P., McAllester, D., & Ramanan, D. (2008). A discriminatively trained, multiscale, deformable part model. 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1–8. https://doi.org/10.1109/CVPR.2008.4587597
Girshick, R. B., Felzenszwalb, P. F., & McAllester, D. (2011). Object Detection with Grammar Models. In Curran Associates Inc (Ed.), Annual Conference on Neural Information Processing Systems 2011, NIPS 2011 (pp. 442–450). Curran Associates Inc. https://dl.acm.org/doi/10.5555/2986459.2986509
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 580–587. https://doi.org/10.1109/CVPR.2014.81
Hasibuan, S. H., Andriani, T., Darmawan, I., & Hidayatullah, M. (2023). Rancang Bangun Alat Pengusir Hama Monyet Pada Ladang Jagung Design of a Monkey Pest Repellent Tool in Corn Fields. Jurnal Elektronika, Sains Dan Sistem Energi, 02(02), 130–136.
Herdianto, H., & Nasution, D. (2023). Rancang Bangun Sistem Pengusir Hama Monyet Pada Perkebunan Kopi Menggunakan Arduino Uno. Prosiding Seminar Nasional Unars, 624–633. https://unars.ac.id/ojs/index.php/prosidingSDGs/article/view/3475
Hidayati, Q., Jamal, N., Veronika, M., & Dzulfiqar Raihan, A. Z. (2024). Sistem Keamanan Trafo Gardu Induk 150 KV Dari Hewan Kera di PT PLN UPT Kaltimtara. Journal of Electrical Engineering and Computer (JEECOM), 6(2), 417–426. https://doi.org/10.33650/jeecom.v6i2.9564
Sahri, M. S., Salsabila, A., Hasanah, L. M., Daulay, A. M., Al Hazza, M. R. R., & Rahmadani, K. (2024). Pencegahan Hama Kera Ekor Panjang Sebagai Upaya Mengatasi Permasalahan Petani di Dusun Tekik, Desa Ngloro, Kapanewon Saptosari, Kabupaten Gunungkidul. Welfare : Jurnal Pengabdian Masyarakat, 2(3), 606–610. https://doi.org/10.30762/welfare.v2i3.1585
Statistik, B. P. (2025). Ekspor Kopi Menurut Negara Tujuan Utama, 2000-2024. https://www.bps.go.id/id/statistics-table/1/MTAxNCMx/ekspor-kopi-menurut-negara-tujuan-utama--2000-2024.html
Tamia, E. B., & Zafia, A. (2022). Rancang Bangun Prototype Pengusir Hama Kera Pada Perkebunan Berbasis Internet Of Things. LEDGER : Journal Informatic and Information Technology, 1(1), 25–38. https://doi.org/10.20895/ledger.v1i1.775
Viola, P., & Jones, M. (2001). Rapid Object Detection using a Boosted Cascade of Simple Features. Computer Society Conference on Computer Vision and Pattern Recognition, 1–9. https://doi.org/10.1109/CVPR.2001.990517
Viola, P., & Jones, M. J. (2004). Robust Real-Time Face Detection. International Journal of Computer Vision, 57(2), 137–154. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
Zou, Z., Shi, Z., Guo, Y., & Ye, J. (2019). Object Detection in 20 Years: A Survey. May, 1–39. http://arxiv.org/abs/1905.05055
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Herdianto Herdianto (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
