Diseased Portion Cassification & Recognition of Cotton Plants using Convolution Neural Networks
Prashant Udawant1, Pravin Srinath2
1Prashant Udawant, Assistant Professor, Department of Mechanical Engineering, Narsee Monjee Institute of Management Studies.
2Pravin Srinath, Associate Professor, Department of Mechanical Engineering, Narsee Monjee Institute of Management Studies.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 3492-3496 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9526088619/2019©BEIESP | DOI: 10.35940/ijeat.F9526.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cotton plant is one of the cash crops in India. For more profit its intense care is necessary. Many researchers are using machine learning for early detections of cotton plant disease. Convolution neural network (CNN) is a deep feed forward artificial neural network. This algorithm is little faster as compared to other classification algorithms. In this paper, CNN is used for classification of the diseased portion of cotton plant images. The result shows that the model used classifies the healthy and diseased cotton leaves more accurately.
Keywords: Convolution Neural Network (CNN), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Rectified Linear Unit (RELU)