Innovation in Plant Pest and Disease Detection and Control Strategies in The Era of Smart Agriculture 2025–2026

Penelitian

Authors

  • Safarinda Nurdianawati Halim Sanusi University PUI

DOI:

https://doi.org/10.31004/jerkin.v4i3.5668

Keywords:

Smart Agriculture, Pests, Diseases, Artificial Intelligence, IPM

Abstract

Perubahan iklim, intensifikasi pertanian, dan mobilitas global telah meningkatkan kompleksitas serangan hama dan penyakit tanaman pada periode 2025–2026. Studi ini bertujuan untuk menganalisis tren peningkatan kejadian hama dan efektivitas teknologi deteksi berbasis kecerdasan buatan (AI) dalam sistem pertanian cerdas. Metode penelitian menggunakan pendekatan kuantitatif deskriptif dengan analisis tren data dari tahun 2023–2026. Hasil menunjukkan peningkatan kejadian hama dari 38% (2023) menjadi 50% (2026). Sementara itu, akurasi deteksi berbasis AI meningkat secara signifikan dari 82% menjadi 96%. Temuan ini menunjukkan bahwa meskipun tekanan hama meningkat akibat perubahan iklim, integrasi teknologi digital dapat meningkatkan respons dan efektivitas pengendalian. Strategi pengendalian berbasis data terintegrasi merupakan solusi kunci dalam mendukung ketahanan pangan berkelanjutan.

References

Bebber, D. P. (2021). Range-expanding pests and pathogens in a warming world . Nature Climate Change, 11(4), 300–308. https://doi.org/10.1038/s41558-021-01013-9

Collier, R., & Van Steenwyk, R. (2021 ). Sustainable crop protection under climate change . CABI Publishing.

Creswell, J. W. (2019). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.

Dhawan, A. K. (2022). Integrated pest management: Concepts and practice (2nd ed.). Springer.

Li, Y., Zhang, C., & Wang, J. (2023). UAV-based crop disease detection using convolutional neural networks . Agricultural Systems, 205, 103556. https://doi.org/10.1016/j.agsy.2023.103556

Lowenberg-DeBoer, J., & Erickson, B. (2020). Precision agricultural technology for crop farming . American Society of Agronomy.

Mahlein, A. K. (2020 ). Plant disease detection by imaging sensors . Plant Pathology, 69(1), 3–16. https://doi.org/10.1111/ppa.13126

Savary, S., Willocquet, L., Esker, P., McRoberts, N., & Nelson, A. (2022 ). The global burden of pathogens and pests on major food crops. Annual Review of Phytopathology, 60, 221–246. https://doi.org/10.1146/annurev-phyto-080516-035830

Shah, D., Trivedi, R., & Patel, M. (2024 ). Artificial intelligence and real-time monitoring in precision agriculture. Computers and Electronics in Agriculture, 218, 108123. https://doi.org/10.1016/j.compag.2024.108123

Sugiyono. (2020). Quantitative, qualitative, and R&D research methods (2nd ed.). Alfabeta.

Zhang, L., & Wang, H. (2022 ). Artificial intelligence in agriculture: Applications and challenges . Springer Nature.

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Published

25-02-2026

How to Cite

Nurdianawati, S. (2026). Innovation in Plant Pest and Disease Detection and Control Strategies in The Era of Smart Agriculture 2025–2026: Penelitian. Jurnal Pengabdian Masyarakat Dan Riset Pendidikan, 4(3), 21431–21436. https://doi.org/10.31004/jerkin.v4i3.5668