Identifikasi penyakit tanaman tomat melalui citra daun menggunakan DenseNet201 - Okamisar; Hayami, Regiolina; Fuad, Evans (Universitas Muhammadiyah Riau, 2025)
This study focuses on implementing the DenseNet201 algorithm for disease classification in tomato plants using leaf images from PlantVillage dataset. The agricultural sector plays a central role in the Indonesian economy, with tomatoes being one of the important horticultural crops. However, tomato productivity is often hindered by various plant diseases. Accurate disease diagnosis is crucial for improving production stability. Image processing-based approaches, such as Convolutional Neural Network (CNN), have facilitated effective plant disease diagnosis. In this study, the PlantVillage dataset consisting of 18,835 tomato leaf images is utilized. The data is divided into training (10,000 images), validation (7,000 images), and test (500 images) sets. A classification model is constructed using the DenseNet201 architecture with some modifications. The results show that the DenseNet201 model achieves an accuracy of 95.20% on the testing data, with an overall F1-score of 0.95. Compared to previous studies using VGG16 (77.2%), InceptionV3 (63.4%), and MobileNet (63.75%), the DenseNet201 model demonstrates a significant performance improvement. This study concludes that DenseNet201 is highly effective in classifying tomato plant diseases and has the potential to be implemented in widespread plant disease diagnosis applications.