Application of CNN VGG16 for Digital Image-based Rice Leaf Disease Classification with Optimizer Analysis and Real-Time Validation
DOI:
https://doi.org/10.54082/jiki.287Keywords:
CNN, deep learning, image classification, optimizer, rice leaf disease, VGG16Abstract
Rice leaf diseases can significantly reduce yields, while manual identification takes a long time and risks producing errors. This research proposes a deep learning-based rice leaf disease classification system by utilizing the VGG16 Convolutional Neural Network (CNN) architecture through a transfer learning approach. The dataset was obtained from Kaggle with five categories, namely bacterial leaf blight, rice blast, brown spot, healthy, and tungro, which were processed through normalization and augmentation before being divided into training and validation data. The model was trained using two optimizers, Adam and Adadelta, with epoch variations of 20, 25, 35, and 50 to compare their performance. Experimental results showed that Adam produced the best validation accuracy of 96.24% and testing accuracy of 97.17%, with an average F1-score of 0.98, while Adadelta only achieved a maximum validation accuracy of 85.3%. Evaluation using confusion matrix and classification report further confirmed the reliability of the model, with Adam even achieving 100% accuracy on real-time testing. These findings show that the VGG16 CNN with Adam optimizer is capable of detecting rice leaf diseases quickly and accurately, thus contributing to early detection in precision agriculture and supporting food security.
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