Potensi Penerapan Smart-Trap Berbasis IoT untuk Monitoring Hama dalam Pengelolaan Hama Terpadu pada Budidaya Cabai Lahan Kering di Nusa Tenggara Barat
Diterbitkan:
2025-12-30Terbitan:
Vol 1 No 3 (2025): JOURNAL OF MULTIDISCIPLINARY SCIENCE AND NATURAL RESOURCE MANAGEMENTKata Kunci:
Smart-Trap, Internet of Things (IoT), Monitoring Hama, Cabai Lahan Kering, Pengelolaan Hama Terpadu (PHT), NTBArtikel
Unduhan
Cara Mengutip
Abstrak
Budidaya cabai lahan kering di Nusa Tenggara Barat (NTB) menghadapi peningkatan serangan hama akibat kondisi agroklimat ekstrem, sementara monitoring manual sering terlambat dan mendorong penggunaan pestisida berlebihan. Kajian ini bertujuan mengevaluasi potensi smart-trap berbasis Internet of Things (IoT) dalam meningkatkan efektivitas monitoring hama dan mendukung Pengelolaan Hama Terpadu (PHT) pada budidaya cabai lahan kering. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan kerangka PRISMA 2020 terhadap 19 artikel ilmiah terpilih periode 2020–2025. Hasil kajian menunjukkan bahwa integrasi kamera digital, sensor mikroklimat, dan algoritma deep learning mampu mendeteksi hama secara real-time, meningkatkan akurasi pengambilan keputusan, serta menekan penggunaan pestisida. Meskipun demikian, implementasi di NTB masih menghadapi kendala infrastruktur, biaya awal, dan literasi digital petani. Oleh karena itu, diperlukan adaptasi teknologi yang berbiaya rendah, tahan kondisi lahan kering, dan didukung pendampingan penyuluhan agar smart-trap IoT dapat diadopsi secara efektif dan berkelanjutan.
Keyword: Smart-Trap; Internet of Things (IoT); Monitoring Hama; Cabai Lahan Kering; Pengelolaan Hama Terpadu (PHT); NTB.
Referensi
Agustinus Tamba, T., Darwin, S., Solaiman, G., & Reysan, J. (2024). Rancang Bangun Sistem Deteksi Hama Tanaman Whiteflies Berbasis Jaringan Sensor Nirkabel dan Aplikasi Web Design and Development of a Whiteflies Plant Pest Detection System Based on Wireless Sensor Networks and Web Applications. Jurnal Otomasi Kontrol Dan Instrumentasi, 16(2), 2024.
Albanese, A., Nardello, M., & Brunelli, D. (2021). Automated Pest Detection with DNN on the Edge for Precision Agriculture. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 11(3), 458–467. https://doi.org/10.1109/JETCAS.2021.3101740
Amrani A, Sohel F, Diepeveen D, Murray D, Jones MGK. (2023) Insect detection from imagery using YOLOv3-based adaptive feature fusion convolution network. Crop & Pasture Science 74, 615–627. https://doi.org/10.1071/CP21710
Chandra, R., & Collis, S. (2021). Digital agriculture for small-scale producers. Communications of the ACM, 64(12), 75–84. https://doi.org/10.1145/3454008
da Silva Vieira, G., Rocha, B. M., Fonseca, A. U., de Sousa, N. M., Ferreira, J. C., Cabacinha, C. D., & Soares, F. (2022). Automatic detection of insect predation through the segmentation of damaged leaves. Smart Agricultural Technology, 2(April), 100056. https://doi.org/10.1016/j.atech.2022.100056
Diller, Y., Shamsian, A., Shaked, B., Altman, Y., Danziger, B.-C., Manrakhan, A., Serfontein, L., Bali, E., Wernicke, M., Egartner, A., Colacci, M., Sciarretta, A., Chechik, G., Alchanatis, V., Papadopoulos, N. T., & Nestel, D. (2023). A real-time remote surveillance system for fruit flies of economic importance: sensitivity and image analysis. Journal of Pest Science, 96(2), 611–622. https://doi.org/10.1007/s10340-022-01528-x
Eliopoulos, P., Tatlas, N. A., Rigakis, I., & Potamitis, I. (2018). A “smart” trap device for detection of crawling insects and other arthropods in urban environments. Electronics (Switzerland), 7(9). https://doi.org/10.3390/electronics7090161
Fernando H Iost Filho, Wieke B Heldens, Zhaodan Kong, Elvira S de Lange, Drones: Innovative Technology for Use in Precision Pest Management, Journal of Economic Entomology, 113(1), 1–25, https://doi.org/10.1093/jee/toz268
Fuentes, S., Tongson, E., Unnithan, R. R., & Gonzalez Viejo, C. (2021). Early Detection of Aphid Infestation and Insect-Plant Interaction Assessment in Wheat Using a Low-Cost Electronic Nose (E-Nose), Near-Infrared Spectroscopy and Machine Learning Modeling. Sensors, 21(17), 5948. https://doi.org/10.3390/s21175948
Grijalva, Ivan, Spiesman, Brian J., McCornack, Brian. (2023). Image classification of sugarcane aphid density using deep convolutional neural networks. Smart Agricultural Technology, 3, 100089. https://doi.org/https://doi.org/10.1016/j.atech.2022.100089
Guo, Q., Wang, C., Xiao, D., & Huang, Q. (2023). Automatic monitoring of flying vegetable insect pests using an RGB camera and YOLO-SIP detector. Precision Agriculture. https://doi.org/10.1007/s11119-022-09952-w
Hunter, J. E., Gannon, T. W., Richardson, R. J., Yelverton, F. H., & Leon, R. G. (2020). Integration of remote‐weed mapping and an autonomous spraying unmanned aerial vehicle for site‐specific weed management. Pest Management Science, 76(4), 1386–1392. https://doi.org/10.1002/ps.5651
Hutasoit, R. T., Triwidodo, H., & Anwar, R. (2018). Biologi dan statistik demografi Thrips parvispinus Karny (Thysanoptera: Thripidae) pada tanaman cabai (Capsicum annuum Linnaeus). Jurnal Entomologi Indonesia, 14(3), 107. https://doi.org/10.5994/jei.14.3.107
Liu, Huajian, Chahl, Javaan Singh (2021). Proximal detecting invertebrate pests on crops using a deep residual convolutional neural network trained by virtual images. Artificial Intelligence in Agriculture, 5, 13-23. https://doi.org/https://doi.org/10.1016/j.aiia.2021.01.003
Mendoza, Q. A., Pordesimo, L., Neilsen, M., Armstrong, P., Campbell, J., & Mendoza, P. T. (2023). Application of Machine Learning for Insect Monitoring in Grain Facilities. AI, 4(1), 348-360. https://doi.org/10.3390/ai4010017
Prabhakar, M., Rao, M. S., Timmanna, H., Prasad, T. V., & Kumar, N. V. (2024). Climate Change and its Impact on Pests of Dryland Crops. Indian Journal of Dryland Agricultural Research and Development, 39(2spl), 121–127. https://doi.org/10.5958/2231-6701.2024.00025.7
Praseptiawan, M., Untoro, M. C., Millennium, L. V., & Affandi, M. (2022). Sistem Informasi Monitoring Lahan Pertanian dan Pengusiran Hama Berbasis Internet of Thing. ILKOMNIKA: Journal of Computer Science and Applied Informatics, 4(2), 162–170. https://doi.org/10.28926/ilkomnika.v4i2.460
Qiulin WU Juan ZENG Kongming WU (2022). RESEARCH AND APPLICATION OF CROP PEST MONITORING AND EARLY WARNING TECHNOLOGY IN CHINA. Frontiers of Agricultural Science and Engineering, 9, 19-36. https://doi.org/https://doi.org/10.15302/J-FASE-2021411
Ramalingam, B., Mohan, R. E., Pookkuttath, S., Gómez, B. F., Sairam Borusu, C. S. C., Wee Teng, T., & Tamilselvam, Y. K. (2020). Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT. Sensors, 20(18), 5280. https://doi.org/10.3390/s20185280
Rossi, Vittorio, Caffi, Tito, Salotti, Irene, Fedele, Giorgia (2023). Sharing decision-making tools for pest management may foster implementation of Integrated Pest Management. Food Security, 15(6), 1459-1474. https://doi.org/10.1007/s12571-023-01402-3
Rustia, Dan Jeric Arcega, Lin, Chien Erh, Chung, Jui-Yung, Zhuang, Yi-Ji, Hsu, Ju-Chun, Lin, Ta-Te (2020). Application of an image and environmental sensor network for automated greenhouse insect pest monitoring. Journal of Asia-Pacific Entomology, 23, 17-28. https://doi.org/10.1016/j.aspen.2019.11.006
Schrader, M. J., Smytheman, P., Beers, E. H., & Khot, L. R. (2022). An Open-Source Low-Cost Imaging System Plug-In for Pheromone Traps Aiding Remote Insect Pest Population Monitoring in Fruit Crops. Machines, 10(1), 52. https://doi.org/10.3390/machines10010052
Segalla, Andrea, Fiacco, Gianluca, Tramarin, Luca, Nardello, Matteo, Brunelli, Davide (2020). Neural networks for Pest Detection in Precision Agriculture. 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). https://doi.org/10.1109/MetroAgriFor50201.2020.9277657
Betti Sorbelli, Francesco, Palazzetti, Lorenzo, Pinotti, Cristina M. (2023/10/1). YOLO-based detection of Halyomorpha halys in orchards using RGB cameras and drones. Computers and Electronics in Agriculture, 213, 108228. https://doi.org/10.1016/j.compag.2023.108228
Tiwari, A. K. (2024). Insect Pests in Agriculture Identifying and Overcoming Challenges through IPM. Archives of Current Research International, 24(3), 124–130. https://doi.org/10.9734/acri/2024/v24i3651
S. Verma, S. Tripathi, A. Singh, M. Ojha, R. R. Saxena (2021). Insect Detection and Identification using YOLO Algorithms on Soybean Crop. TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), JA - TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON)(SN - 2159-3450), 272-277. https://doi.org/10.1109/TENCON54134.2021.9707354
Yasir, M., Hossain, A., & Pratap-Singh, A. (2025). Pesticide Degradation: Impacts on Soil Fertility and Nutrient Cycling. Environments, 12(8), 272. https://doi.org/10.3390/environments12080272
Zhao, Nan, Zhou, Lei, Huang, Ting, Taha, Mohamed Farag, He, Yong, Qiu, Zhengjun (2022/11/1). Development of an automatic pest monitoring system using a deep learning model of DPeNet. Measurement, 203, 111970. https://doi.org/10.1016/j.measurement.2022.111970.
Biografi Penulis
Lalu kurniawan, MPLK Pasca Sarjana Universitas Mataram
Muhammad Sarjan, Program Studi Magister Pertanian Lahan Kering, Pascasarjana Universitas Mataram, Mataram, Indonesia.
Lisensi
Hak Cipta (c) 2025 Lalu kurniawan, Muhammad Sarjan

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