Abstract
In the present scenario, disease identification is one of the prominent research in the field of the agricultural revolution. Early disease identification helps us to increase the productivity, quality of crop, and reduce the overall cost of the crop production. In this context, the proposed research work helps to identify tomato plant leaf disease in the early stage. Tomato is the most important agricultural product in the world. Currently, deep learning models are playing an important role in object classification. In this work, AlexNet deep learning model has been implemented with the change layering structure for the desired result. For the investigation of the proposed method, two different types of databases are used, having the 500 sample unhealthy images with different types of leaf disease and the 100 sample healthy image. The Deep learning model also tests the different convolution filter and max pooling filter with in various shapes of 32*32*3, 64*64*3, and 128*128*3. The proposed work achieves the best accuracy of 96.84% for the performance evaluation of disease classification.
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Singh, R.K., Tiwari, A., Gupta, R.K. (2023). Deep Transfer Modeling for Classification and Identification of Tomato Plant Leaf Disease. In: Shukla, P.K., Singh, K.P., Tripathi, A.K., Engelbrecht, A. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-19-7892-0_25
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DOI: https://doi.org/10.1007/978-981-19-7892-0_25
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