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PLANT DISEASE DETECTION WITH CONVOLUTIONAL NEURAL NETWORK
ISSN: 2582 - 9130
Publisher: author
PLANT DISEASE DETECTION WITH CONVOLUTIONAL NEURAL NETWORK
Indexed in
Technology and Engineering
ARTICLE-FACTOR
1.3
Article Basics Score: 3
Article Transparency Score: 2
Article Operation Score: 2
Article Articles Score: 3
Article Accessibility Score: 2
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International Category Code (ICC):
ICC-1802
Publisher: Krishma Publication
International Journal Address (IAA):
IAA.ZONE/2582384289130
eISSN
:
2582 - 9130
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Abstract
Pest damage to plants and crops has an impact on the nation's agricultural output. In most cases, farmers or professionals watch the plants carefully for signs of illness. However, this procedure is frequently timeconsuming, costly, and unreliable. Results from automatic detection employing image processing methods are quick and precise. This study uses deep convolutional networks to establish a new method for developing illness detection models that is backed by leaf image categorization. The area of precision agriculture has a possibility to grow and improve the practice of precise plant protection as well as the market for computer vision applications. A quick and simple system is made possible by the approach utilized and a wholly original manner of training. Keywords: Plant Disease Detection, Machine Learning, Image Processing, Deep Learning, Convolutional Neural Network
