Abstract
In this paper, we have discussed the design of the system that associates the Deep Convolutional Neural Network that can estimate the identity of the disease from the symptoms. Identifying the disease from plants and discovering the possibility that plant is either infected or not, will decrease the likelihood of risk due to such infection by taking appropriate steps against it. Proposed CNN is trained and build with higher precision and accuracy that associate the automatic detection of the disease from the plant leaves in preference of experienced human inspection. Designing the pure CNN that can identify the healthy plant species and infected plants with an accuracy of the 99% and which can avoid the significant loss of farmers. Proposed CNN includes the multiple layers that are trained intensely to identify the convoluted features of the images. The composition of the CNN model is done over the 35,000 training images with testing set from the same distribution with 4400 images. Detailed results are discussed in the paper.
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“The NVIDIA Corporation donated the Titan XP used for this research.” or “We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan XP GPU used for this research.”
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Desai, S., Nayak, R., Patel, R. (2020). Identifying Plant Diseases Using Deep Convolutional Neural Networks. In: Mehta, A., Rawat, A., Chauhan, P. (eds) Recent Advances in Communication Infrastructure. Lecture Notes in Electrical Engineering, vol 618. Springer, Singapore. https://doi.org/10.1007/978-981-15-0974-2_9
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DOI: https://doi.org/10.1007/978-981-15-0974-2_9
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