PENERAPAN CONVOLUTIONAL NEURAL NETWORK UNTUK IDENTIFIKASI PENYAKIT PADA TANAMAN PADI DARI CITRA DAUN MENGGUNAKAN MODEL RESNET-101

Muhammad Sidiq Pramono; Aditya Permana Wibowo

doi:10.46576/djtechno.v5i3.5098

PENERAPAN CONVOLUTIONAL NEURAL NETWORK UNTUK IDENTIFIKASI PENYAKIT PADA TANAMAN PADI DARI CITRA DAUN MENGGUNAKAN MODEL RESNET-101

Muhammad Sidiq Pramono, Aditya Permana Wibowo

Abstract

AbstrakPenyakit pada tanaman padi merupakan salah satu faktor utama yang dapat menurunkan produktivitas dan kualitas hasil panen. Jika tidak ditangani dengan tepat, hal ini dapat menyebabkan kerugian ekonomi bagi para petani. Deteksi dini penyakit padi sangat penting, namun identifikasi manual oleh para ahli seringkali memakan waktu dan rentan terhadap kesalahan manusia. Untuk mengatasi masalah ini, penelitian ini mengusulkan penerapan Convolutional Neural Network (CNN) dengan arsitektur ResNet-101 untuk mengklasifikasikan penyakit pada tanaman padi berdasarkan citra daun secara otomatis. Langkah-langkah yang dilakukan meliputi pengumpulan data citra daun padi, preprocessing data, pembagian dataset, pelatihan model CNN, evaluasi model, serta implementasi model ke dalam sistem berbasis web. Hasil penelitian yang dilakukan menunjukkan bahwa model CNN ResNet-101 yang dibangun mampu mencapai akurasi 76.05% dalam mengklasifikasikan 4 kondisi daun padi (sehat, bercak coklat, blast daun, hispa) pada data validasi. Sistem ini diharapkan dapat membantu petani dalam mendeteksi penyakit tanaman padi secara dini dan akurat, sehingga tindakan pencegahan dan penanganan dapat dilakukan dengan tepat waktu.Kata Kunci: Penyakit tanaman padi, Convolutional Neural Network, ResNet-101, Klasifikasi citra, Deteksi Dini.AbstractRice plant diseases are one of the main factors that can reduce productivity and harvest quality. If not handled properly, this can cause economic losses for farmers. Early detection of rice diseases is crucial, however manual identification by experts is often time-consuming and prone to human error. To address this issue, this research proposes the implementation of Convolutional Neural Network (CNN) with ResNet-101 architecture to automatically classify rice plant diseases based on leaf images. The steps involved include collecting rice leaf image data, data preprocessing, dataset splitting, CNN model training, model evaluation, and model implementation into a web-based system. The research results show that the developed CNN ResNet-101 model achieved 76.05% accuracy in classifying 4 rice leaf conditions (healthy, brown spot, leaf blast, hispa) on validation data. This system is expected to help farmers detect rice plant diseases early and accurately, so that preventive measures and treatments can be carried out in a timely manner.Keywords: Rice plant diseases, Convolutional Neural Network, ResNet-101, Image classification, Early Detection.

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DOI: https://doi.org/10.46576/djtechno.v5i3.5098