Abstract
Various diseases of plants are the main reason behind reducing production, resulting in a significant loss in agriculture. The evolution of deep learning and its diversification use in different fields extends the opportunity to recognize plant disease accurately. The challenges in plant disease recognition are limited to homogeneous background and high memory for a large number of parameters. In this work, a dataset of 2880 tomato plant images is used to train the depthwise separable convolution-based model to reduce the trainable parameters for memory restriction devices such as mobile. An independent set of test images, including 612 tomato plant images of nine diseases, is used to assess the model under different illumination and orientations. Depthwise Separable Convolution-based tomato leaf disease recognition model entitled reduced MobileNet outperforms according to the trade-off among accuracy, computational latency, and scale of parameters, and achieves 98.31% accuracy and 92.03% F1-score.
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Hossain, S.M.M., Aashiq Kamal, K.M., Sen, A., Deb, K. (2022). Tomato Leaf Disease Recognition Using Depthwise Separable Convolution. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing & Optimization. ICO 2021. Lecture Notes in Networks and Systems, vol 371. Springer, Cham. https://doi.org/10.1007/978-3-030-93247-3_33
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DOI: https://doi.org/10.1007/978-3-030-93247-3_33
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