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Machine Learning Technique for Crop Disease Prediction Through Crop Leaf Image |
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PP: 149-158 |
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doi:10.18576/amis/160202
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Author(s) |
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S. Nandhini,
K. Ashokkumar,
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Abstract |
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| Plant diseases are a huge danger to food security, yet owing to the lack of infrastructure in several regions of the world, timely detection is challenging. Smartphone-assisted detection of disease is now achievable thanks to a combination of rising global smartphone usage & recent advancements in machine vision facilitated by depth learning. They trained a depth convolutional neural network to detect 16 crops & 25 diseases utilizing a public dataset for 64,412 pictures of damaged & normal leaf tissue taken under controlled settings (absence thereof). On the held-out testing set, the training set achieved an accuracy of 99.35 percent, showing the practicality of this strategy. The method of training deep learning techniques increasingly vast & available to the public specific sequence points to a clear route to widespread global smartphone assisted plant disease detection.
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