• Aashritha H.H
• Rachana A
• Bhoomika B.V
• Chitrashree P
• Prof Rakshatha S

When plants are infected it affects the crop yield and It is important to detect the disease at an early stage for effective crop yield. The different types of diseases like bacterial, fungal, Septoria, leaf mold and viral diseases affect the crop quality of tomatoes. Usually farmers observe the plants and identify diseases alternatively we can use Automatic methods for classification of plant diseases which helps in taking action after detecting the symptoms of leaf diseases. In this paper we present a Convolutional Neural Network (CNN) model algorithm-based method for tomato leaf disease detection and classification. The dataset contains 2500 images of tomato leaves with 5 symptoms of diseases. We have modeled a CNN for automatic feature extraction and classification. Color information is actively used for plant leaf disease researches. In our model, the filters are applied to three channels based on RGB components. The experimental results validate that the proposed method effectively recognizes 5 different types of tomato leaf diseases.

CNN, early stage, automatic feature extraction, classification, leaf disease.

"LEAF DISEASE DETECTION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.181-185, June-2022, Available :http://www.jetir.org/papers/JETIRFM06033.pdf

"LEAF DISEASE DETECTION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. pp181-185, June-2022, Available at : http://www.jetir.org/papers/JETIRFM06033.pdf