• Gangarde Prashant Subhash
• Mayur Goraksha Gaikwad
• Bhagyashri Vitthal Ghangaon
• Kishor Sadafale

Tomato leaf diseases can have a significant impact on crop yield and cause economic losses for farmers worldwide. Early detection and diagnosis of these diseases are crucial for effective management and control. This research paper proposes a deep learning-based approach for detecting tomato leaf diseases. The method involves training a convolutional neural network (CNN) model using a dataset of tomato leaf images with various disease symptoms. The transfer learning approach was used to fine-tune a pre-trained VGG-16 model for this task. The proposed model achieved a 97.26% accuracy rate on the test set, surpassing other state-of-the-art methods. These results demonstrate the effectiveness of using deep learning for tomato leaf disease detection and suggest potential practical applications in the agricultural industry.

Leaf diseases detection, image processing, convolutional neural networks, feature extraction, deep learning, clustering, classification.

"Tomato Leaf Diseases Detection Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.g215-g221, May-2023, Available :http://www.jetir.org/papers/JETIR2305631.pdf

"Tomato Leaf Diseases Detection Using Deep Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppg215-g221, May-2023, Available at : http://www.jetir.org/papers/JETIR2305631.pdf