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Paper Title
A DEEP LEARNING BASED APPROACH FOR BANANA PLANT LEAF DISEASES CLASSIFICATION AND ANALYSIS
Authors
Nidhi Kamath, Pallavi Mali, Akshara Gupta , Prof. D. D. Pukale
Keywords
Banana plant diseases, Deep learning, Classification, Convolutional Neural Network (CNN), Ratio of Infected Area (RIA), K- Nearest Neighbor (KNN), Scale Invariant Feature Transform (SIFT), Rectified non-linear activation function (ReLU).
Abstract
India is a country where agriculture is very important sector. The production and profit gained from agriculture mainly depends on the quality and quantity of the crop being grown. For this it is very important that the plants should be protected from any of disease that may adversely affect the quality and quantity of the crop. Thus, it is very important to detect the disease affected crop if any and diagnosis of the disease at the early stage itself. This can reduce the loss of the crop. In our project, a deep-learning approach is being proposed to detect and classify banana leaf diseases. In particular, we make use of the LeNet architecture as a convolutional neural network to classify image data sets. Our system can efficiently predict the banana leaf disease and will suggest remedies for the disease being predicted
IJCRT's Publication Details
Unique Identification Number - IJCRT1807147
Paper ID - 187271
Page Number(s) - 242-249
Pubished in - Volume 6 | Issue 2 | April 2018
DOI (Digital Object Identifier) -
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Nidhi Kamath, Pallavi Mali, Akshara Gupta , Prof. D. D. Pukale, "A DEEP LEARNING BASED APPROACH FOR BANANA PLANT LEAF DISEASES CLASSIFICATION AND ANALYSIS", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.6, Issue 2, pp.242-249, April 2018, Available at :http://www.ijcrt.org/papers/IJCRT1807147.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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