Leaf Disease detection from tomato images using deep learning techniques

The agricultural industry is vital for providing high-quality food and driving the development of economies and populations. Plant diseases can lead to substantial reductions in food output and the loss of species diversity. Timely detection of plant diseases through precise or automated methods can improve food quality and reduce financial losses. Recent advancements in deep learning have significantly enhanced the accuracy of identifying images and detecting objects. Therefore, our study employed pre-trained models based on convolutional neural networks (CNNs) for effective identification of tomato diseases. The experiments were carried out using the popular PlantVillage dataset in ۱۰ classes. The performance of the model was evaluated through classification accuracy and Our method demonstrated a remarkable ۹۹.۵۸% test accuracy rate, surpassing other works. This confirms the precision of our approach in detecting and classifying diseases in tomato plants.

Keywords:

deep learning , transfer learning , plant disease , leaf disease