Deteksi Real-Time Hama dan Penyakit Jamur pada Daun Tanaman Menggunakan Deep Learning Berbasis YOLO

Reni Yunita; Egi Dio Bagus Sudewo; Bela Astuti

doi:10.47065/mis.v4i3.2882

Reni Yunita * Universitas Royal, Indonesia
Egi Dio Bagus Sudewo Universitas Royal, Indonesia
Bela Astuti Universitas Royal, Indonesia

DOI: https://doi.org/10.47065/mis.v4i3.2882

Keywords: Deteksi Objek Real-Time; Deep Learning; Deteksi Hama; Penyakit Jamur Tanaman; Pengolahan Citra

Abstract

Serangan hama pertanian dapat menurunkan produksi tanaman secara signifikan dan menghambat kegiatan pertanian. Kesulitan mendeteksi hama secara manual sejak tahap awal membuat petani sering menggunakan pestisida secara berlebihan, yang dapat menyebabkan pencemaran lingkungan dan risiko terhadap kesehatan. Untuk mengatasi permasalahan tersebut, berbagai sistem telah dikembangkan untuk membantu mendeteksi hama sejak dini sehingga petani dapat mengetahui lokasi hama secara lebih tepat. Namun, proses identifikasi di lapangan yang masih dilakukan secara manual sering memerlukan waktu lama, tenaga yang besar, serta rentan terhadap kesalahan identifikasi. Selain itu, beberapa sistem yang telah dikembangkan masih memiliki keterbatasan, seperti belum mampu melakukan deteksi secara real-time dan belum dilengkapi dengan sistem pemantauan berbasis web. Penelitian ini mengusulkan sistem deteksi hama dan penyakit jamur pada daun tanaman secara real-time menggunakan pendekatan deep learning berbasis arsitektur YOLO26. Model YOLO26 dipilih karena memiliki kemampuan deteksi objek yang cepat dan efisien sehingga cocok untuk aplikasi pemantauan pertanian secara real-time. Dataset yang digunakan terdiri dari citra daun tanaman yang telah dianotasi ke dalam dua kelas objek, yaitu pest dan fungus. Hasil pengujian menunjukkan bahwa model yang diusulkan mampu mencapai precision sebesar 82%, recall sebesar 71%, dan mAP@0.5 sebesar 78%, dengan nilai mAP@0.5–0.95 sebesar 37,4%. Selain itu, model memiliki waktu inferensi sekitar 12,1 ms/citra, sehingga mampu melakukan deteksi secara real-time. Secara keseluruhan, sistem yang dikembangkan berpotensi membantu petani dalam melakukan pemantauan tanaman secara otomatis dan mendeteksi serangan hama serta penyakit jamur sejak dini, sehingga dapat mengurangi kerusakan tanaman, menekan penggunaan pestisida secara berlebihan.

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