International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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Original Research Articles                      Volume : 9, Issue:9, September, 2020

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : /
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2020.9(9): 2112-2117

Automatic Pest Identification for Cotton Crop Using Convolution Neural Network
Kalpana1*, K. Senguttuvan2 and P. Latha3
1Computer Science, Department of Social Science, ADAC&RI, Tiruchirappalli, India
2Entomology and 3Pathology, Department of Cotton, Coimbatore
Tamil Nadu Agricultural University, Tamil Nadu, India
*Corresponding author

Deep learning is the modern technique used for image processing. Particularly Convolutional Neural Networks (CNNs), is used for automatic identification of cotton Plant disease. Disease in cotton can interfere with the production of cotton and makes a distress to the country’s economy. To manage the disease in cotton, an accurate diagnosis is essential. In this paper, to diagnosis the disease in cotton a research was carried out to automatically identify the cotton plant disease using Convolutional Neural Networks. Dataset with around 13,372 images for three diseases such as Bacterial Blight, Anthracnose, and Leafhopper are collected from cotton field. Convolutional Neural Networks is used to for both recognition and classification of three cotton disease images captured from farmer’s field. The experimental result showed affirmative output of approximately 93.89 % accuracy of recognition of cotton plant disease using python programming.

Keywords: Bacterial blight, Anthracnose, Leafhopper, Convolutional neural networks, Deep learning, Cotton plant disease

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How to cite this article:

Kalpana, M., K. Senguttuvan and Latha, P. 2020. Automatic Pest Identification for Cotton Crop Using Convolution Neural Network.Int.J.Curr.Microbiol.App.Sci. 9(9): 2112-2117. doi:
Copyright: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.