The Implementation of Deep Learning Method for Disease Detection in Tomato Plants Based on Leaf Images via Web - Putra, Chaeru Rachmadi; Rachman, Amang Sjamsjiar; Suksmadana, I Made Budi (Fidelity : Jurnal Teknik Elektro, 2025)
Agriculture plays a vital role in supporting the economy. Optimally and wisely managed agricultural development can encourage sustainable economic growth and equity. One example is tomato production, which has great potential to be developed. In 2021, the production of tomatoes in all Indonesian provinces reached a total of 1,114,401 tons. However, tomato production often decreases due to disease attacks on plants. Therefore, this research aimed to identify plant diseases by utilizing deep learning methods applied to web applications, so that they can be easily accessed by farmers. Its use only requires uploading images of plants to be identified into the web application. Based on the results of training and testing conducted at Google Collaboratory using two model architectures, the findings highlight that VGG16 and DenseNet121, the DenseNet121 architecture provides higher accuracy reaching 100%, while VGG16 reaches 98.58%. In addition, in web application implementation and testing with primary data, the DenseNet121 model also showed high accuracy of 92%.