The Potential of Implementing IoT-Based Smart Traps for Pest Monitoring in Integrated Pest Management of Dryland Chili Cultivation in West Nusa Tenggara

Authors

Lalu kurniawan , Muhammad Sarjan

Published:

2025-12-30

Issue:

Vol. 1 No. 3 (2025): JOURNAL OF MULTIDISCIPLINARY SCIENCE AND NATURAL RESOURCE MANAGEMENT

Keywords:

Smart-Trap, Internet of Things (IoT), Monitoring Hama, Cabai Lahan Kering, Pengelolaan Hama Terpadu (PHT), NTB

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kurniawan, L., & Sarjan, M. (2025). The Potential of Implementing IoT-Based Smart Traps for Pest Monitoring in Integrated Pest Management of Dryland Chili Cultivation in West Nusa Tenggara. Journal of Multidisciplinary Science and Natural Resource Management , 1(3), 39–46. Retrieved from https://jurnalpasca.unram.ac.id/index.php/jom/article/view/1423

Abstract

Dryland chili cultivation in West Nusa Tenggara (NTB) faces increasing pest attacks due to extreme agro-climatic conditions, while manual monitoring is often delayed and encourages excessive pesticide use. This study aims to evaluate the potential of Internet of Things (IoT)-based smart traps to improve pest monitoring effectiveness and support Integrated Pest Management (IPM) in dryland chili cultivation. The method used was a Systematic Literature Review (SLR) using the PRISMA 2020 framework of 19 selected scientific articles from 2020–2025. The study results indicate that the integration of digital cameras, microclimate sensors, and deep learning algorithms can detect pests in real time, improve decision-making accuracy, and reduce pesticide use. However, implementation in NTB still faces challenges related to infrastructure, initial costs, and farmers' digital literacy. Therefore, adapting low-cost technology that is resilient to dryland conditions and supported by extension support is necessary for the effective and sustainable adoption of IoT smart traps.

Keywords: Smart Trap; Internet of Things (IoT); Pest Monitoring; Dryland Chili; Integrated Pest Management (IPM); NTB.

References

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.

Author Biographies

Lalu kurniawan, MPLK Pasca Sarjana Universitas Mataram

Muhammad Sarjan, Program Studi Magister Pertanian Lahan Kering, Pascasarjana Universitas Mataram, Mataram, Indonesia.

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Copyright (c) 2025 Lalu kurniawan, Muhammad Sarjan

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