vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffb3b42e000000b252100001000500 Agriculture is the primary resource of livelihood, and the economy of our country highly depends on agricultural productivity. For this reason, plant disease detection places a vital role in the agriculture sector. According to one survey, in India, nearly 70% of the population depends on agriculture which is composed of many crops. Disease identification in plants is very challenging for farmers as well as for researchers. We proposed a 24-layer deep learning model in our paper using convolution neural networks (CNN) for the detection of corn leaf diseases by using real time image dataset as input. The CNN model is trained with different corn leaf image samples and model performance is tested and is reported with the evaluation metrics. The obtained results are compared with CNN pre-defined models which shows the superior performance of the proposed model compared to other state-of-the-art approaches.
Kamesh Sonti, M. Thamarai, P. Sudheer Chakravarthi
Sri Vasavi Engineering College, India
Agriculture, Plant disease detection, Deep learning, Convolution neural networks, Corn leaf image
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 13 , Issue: 2 , Pages: 2837 - 2843 )
Date of Publication :
November 2022
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