Pertanian Cerdas: Deteksi Serangga Bermanfaat bagi Pertanian dan Perkebunan Mendukung Ketahanan Pangan Nasional di Indonesia
Smart Agriculture: Detecting Beneficial Insects for Farms and Plantations to Support National Food Security in Indonesia
Abstract
Smart Farming menggunakan teknologi digital untuk meningkatkan efisiensi dan hasil panen. Permasalahan yang teridentifikasi ketika penggunaan mart farming adalah iklim tidak menentu, krisis regenerasi petani, degradasi lahan,pemborosan sumber daya, serangan hama mendadak: Deteksi terlambat memicu gagal panen total. Tujuan penelitian di Desa Aek Parira pada bulan Januari hingga Maret 2026 adalah optimasi hasil panen, efisiensi biaya operasional dengan mengurangi pemakaian air, pupuk, dan tenaga kerja, keberlanjutan lingkungan,transparansi data, mitigasi risiko gagal panen. Penggunaan Smart Farming di Bidang Pertanian, seperti: Sensor tanah IoT, sistem irigasi otomatis, drone pemetaan, aplikasi prediksi cuaca melalui lokasi Icare, SmartFram Indonesia, Green Harvest, Agrigrow, Fresh Field, Pest Guard, Sipatuh, dan Tani Cerdas.Penggunaan di Bidang Perkebunan melalui drone sprayer, panen berbasis robotik, sensor pelacak pohon, dan sistem pelacakan barcode dalam bentuk aplikasi Habibii Garden, Plantation Smart, Agro Estate, Palm Care, Cacao Future, Estate Grow, Green Plantation, dan Nusantara Plantation. Penggunaan di bidang ketahanan pangan dapat menghitung prediksi stok nasional, silo penyimpanan pintar, sistem peringatan dini, dan ketertelusuran blokchain yang dapat menjamin keamanan pangan dari manipulasi pasar.
References
Amarathunga, D. C., Ratnayake, M. N., Grundy, J., & Dorin, A. (2022). Towards precision apiculture: Traditional and technological insect monitoring methods in strawberry and raspberry crop polytunnels tell different pollination stories. Computers and Electronics in Agriculture 203, 107462.
Budianta, D., et al. (2024). Enhancing rice production resilience in South Sumatra through climate-smart agriculture practices. Journal of Smart Agriculture and Environmental Technology.
Chakrabarty, S., Shashank, P. R., Deb, C. K., & Dhillon, M. K. (2024). Deep learning-based accurate detection of insects and damage in cruciferous crops using YOLOv5. Smart Agricultural Technology, 100663.
Hasyim, A., Riniwati, H., & Atikawati, D. (2025). Climate smart agriculture sustainability: A multidimensional assessment. The Journal of Indonesia Sustainable Development Planning 6(3): 428-447.
Kiobia, D. O., et al. (2023). A review of successes and impeding challenges of IoT-based insect pest detection systems for estimating agroecosystem health and productivity of cotton. Sensors 23(8): 4127.
Popescu, D., Dinca, A., Ichim, L., & Angelescu, N. (2023). New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks: A review. Frontiers in Plant Science, 14, 1268167.
Rahman, M., et al. (2024). Real-time harmful insect detection framework for smart agriculture using IoT and machine learning. IEEE Computer Society Publication.
Ratnayake, M. N., Dyer, A. G., & Dorin, A. (2021a). Towards computer vision and deep learning facilitated pollination monitoring for agriculture. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2915-2924.
Ratnayake, M. N., Dyer, A. G., & Dorin, A. (2021b). Tracking individual honeybees among wildflower clusters with computer vision-facilitated pollinator monitoring. PLOS ONE 16(2):18-24. e0239504.
Rasyid, H. (2024). The role of digital technology in the transformation of agriculture towards smart farming. Journal of World Science.
Savelli, A., et al. (2021). Climate-smart agriculture in Indonesia. World Bank, CIAT, and CGIAR.
Tamimi, H. A. (2024). Improving agricultural productivity: Strengthening smart farming implementation in Indonesia’s agriculture sector. Environment Education and Conservation 1(2): 79-88.
Teixeira, A. C., et al. (2023). Automatic insect detection using deep learning and computer vision in smart agriculture: A systematic review. Agriculture 13(3): 713.
Venkateswara, S. M., et al. (2025). Deep learning based agricultural pest monitoring and classification for smart farming. Scientific Reports.
Yamin, M., et al. (2025). Analysis of climate-smart agriculture adoption and its impact on food security. Research on World Agricultural Economy.
