Tomato: Different Leaf Disease Detection Using Transfer Learning Based Network
DOI:
https://doi.org/10.13052/jmm1550-4646.18313Keywords:
Transfer learning, Disease detection, Classification, AlexNet, AccuracyAbstract
Plant diseases have a significant effect on crop productivity, financial costs, and output. It is vital to research plant diseases in order to increase agricultural yield. Tomatoes are the world’s most frequently cultivated crop, and they are a staple ingredient in almost every cuisine. After potatoes and sweet potatoes, tomato is the most extensively cultivated vegetable on the planet. India was ranked second in tomato output. Numerous diseases have a detrimental effect on the quantity and quality of the tomato crop. Early disease detection will assist farmers in increasing crop production. The research proposes a transfer learning-based technique for detecting five distinct leaf diseases. AlexNet has been used to detect and classify disease. The simulation results reveal that the method based on transfer learning outperforms the other methods with a classification accuracy of 95.6%.
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