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  Published Paper Details:

  Paper Title

PLANT LEAF DISEASE DETECTION USING CNN

  Authors

  GAURAV SUNIL JOHARI,  TARUNRAJ SHIVRAJ CHAVAN,  PRATIKSHA DIGAMBAR KSHIRSAGAR,  PRATIKSHA BHANUDAS PARKHI

  Keywords

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  Abstract


Smart farming system is an innovative technology that helps improve the quality and quantity of agricultural production in the country. Plant leaf disease has been one of the major threats to food security since long ago because it reduces the crop yield and compromises its quality. diagnosis of accurate diseases has been a major challenge and the recent advances in computer vision made possible by deep learning has paved the way for camera assisted disease diagnosis for plant leaf. It described the innovative solution that provides efficient disease detection and deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various plant leaf diseases. A variety of neuron-wise and layer-wise visualization methods were applied and trained using a CNN, with a publicly available plant disease given image dataset. So, it observed that neural networks can capture the colors and textures of lesions specific to respective diseases upon diagnosis, which can act like human decision-making.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2104549

  Paper ID - 206245

  Page Number(s) - 4575-4577

  Pubished in - Volume 9 | Issue 4 | April 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  GAURAV SUNIL JOHARI,  TARUNRAJ SHIVRAJ CHAVAN,  PRATIKSHA DIGAMBAR KSHIRSAGAR,  PRATIKSHA BHANUDAS PARKHI,   "PLANT LEAF DISEASE DETECTION USING CNN", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 4, pp.4575-4577, April 2021, Available at :http://www.ijcrt.org/papers/IJCRT2104549.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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