<div style="text-align: justify;"><strong>Background: </strong>Tomato mosaic virus (ToMV) is the most common and important pathogen of tomato crop. The infected plants show mosaic, mottle, wrinkling, reduced leaf size and uneven ripening of fruits. ToMV is transmitted mechanically, by cultural operations, farm implements and also through seed.&nbsp;<br /><strong>Methods: </strong>The present study was planned to evaluate environment friendly strategies for the management of ToMV disease. Different non-chemical means <em>viz</em>., Elephant Ear plant extract, Eucalyptus extract, Zinc and Boron solution, NPK solution, Milk and Urea were used on four tomato varieties (Tanja, Chico, Manik and Sahil). All the treatments were used at three different concentrations (1%, 2% and 3%) and replicated three times.&nbsp;<br /><strong>Result:</strong> The maximum disease reduction was recorded in plants treated with NPK solution (16.35%) and minimum in plants treated with Elephant Ear plant extract (29.65%). The maximum mean disease severity was recorded at 1 percent concentration of all the treatments (32.46%) which was statistically significant from that of 2 and 3% (24.74% and 19.23%), respectively. Among the varieties, minimum disease severity was recorded in Manik (24.64%) variety and maximum in Chico (31.27%) variety. All the treatments contributed towards reduction in disease severity of which NPK solution being most efficient.</div>

<div style="text-align: justify;">Usage of Non-chemical Methods to Counter the Damages Caused by Tomato Mosaic Virus</div>

<div><strong>Background: </strong>The growing world population has led to a rising demand for food. Tomatoes, a staple in global diets, are widely used in fresh consumption, sauces and processed products. However, tomato diseases pose a major challenge, significantly reducing yield and crop quality. Timely and accurate identification of these diseases is essential for effective management. This study uses advancements in deep learning to develop a Convolutional Neural Network model. The model classifies tomato leaf conditions into five categories: Healthy leaves (HC), two-spotted spider mite (TSSM), tomato yellow leaf curl disease (TYLCD), tomato mosaic virus (ToMV) and target spot (TS).<br />
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<strong>Methods: </strong>The study utilized a publicly available dataset from Mendeley. Images were preprocessed by resizing, reorienting and enhancing quality, ensuring compatibility with the CNN model. The dataset was divided into an 80:20 ratio for training and validation. The CNN architecture consisted of six convolutional layers with ReLU activation, max-pooling layers and two fully connected layers for classification. Model performance was evaluated using Precision, Recall, F1-score and accuracy metrics.<br />
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<strong>Result: </strong>The CNN model achieved a training accuracy of 93.51% and a validation accuracy of 94.83%. TYLCD had the highest classification precision (98.67%) and recall (99.8%). Overall model accuracy was 95.7%, with macro-average and weighted-average F1-scores of 0.9264 and 0.9567, respectively. The confusion matrix highlighted TYLCD as the most accurately classified disease, while TSSM showed the highest misclassification rate. These results demonstrate the model&rsquo;s potential for reliable tomato disease identification, supporting precision agriculture practices.</div>


Usage of Non-chemical Methods to Counter the Damages Caused by Tomato Mosaic Virus

Prediction of Tomato Leaf Diseases using Computational Convolution Neural Network Method