Pemodelan Machine Learning Image Classification Untuk Deteksi Penyakit Tanaman Padi Sebagai Upaya Menjaga Ketahanan Pangan Di Indonesia

Levina, Anora (2021) Pemodelan Machine Learning Image Classification Untuk Deteksi Penyakit Tanaman Padi Sebagai Upaya Menjaga Ketahanan Pangan Di Indonesia. Technical Report. Pustakawan, Perpustakaan Institut Teknologi Telkom Purwokerto. (Unpublished)

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Abstract

Indonesia is an agricultural country where rice is one of the crops that are mostly grown by Indonesian farmers. Rice is the main food need for Indonesian citizens. However, this food demand is still lower in the production index than it should be. Therefore, it is very important to maintain Indonesia's food security by reducing the risk of crop failure caused by various things, especially rice plant disease. This report discusses the rice leaf disease detection system using a machine learning algorithm that is applied to an android application. This rice leaf disease detection application can detect the three most common types of rice plant diseases: bacterial leaf blight, leaf smut, and brown spot. The detection system created is applied to the Android application by using the camera feature on a smartphone. The machine learning algorithm used is image classification CNN (Convolutional Neural Network). The training accuracy achieved is 98% by transferring learning using MobileNetV2. Keywords: Machine Learning, Convolutional Neural Network, Image Classification, Transfer Learning, Rice Disease Detection.

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