Deteksi Citra Daun untuk Klasifikasi Penyakit Padi menggunakan Pendekatan Deep Learning dengan Model CNN - Rijal, Muhammad; Yani, Andi Muhammad; Rahman, Abdul (LPPM STT Terpadu Nurul Fikri, 2024)
Agriculture is a vital sector related to food security. Rice is one of the productions that currently ranks third behind wheat and corn. However, in 2023, rice production in Indonesia will decrease 2022 by 1.12 million tons of GKG, and Diseases in plants are one of the causes of the reduced quantity of agricultural products. This research aims to detect disease in rice plants using leaf images with three classification classes and a test matrix to measure the model built. This research uses the Convolutional Neural Network (CNN) method to classify rice plants based on leaf images with 3 test scenarios using the Jupyter Notebook text editor tool for system coding. Research results with training show that the CNN model can classify diseases in rice based on leaf images. Of the 3 test scenarios carried out, scenario 2 shows the best results with Epoch 50 with training values ​​from the last Epoch, namely training accuracy 0.9905 and training loss 0.0280 while validation accuracy 0.8000 and The validation loss is 0.9222 with the confusion matrix showing the suitability of predictions based on class with the classification report good recall, precision and f1-score values, namely 1.00.