Rice Disease Classification using Deep Convolutional Neural Network
Tanya Shrivastava1, Malavika S. Pillai2, B. Baranidharan3
1Tanya Shrivastava, Student, Department of Computer Science and Engineering, Kattankulathur Campus, SRM Institute of Science and Technology.
2Malavika S. Pillai*, Student, Department of Computer Science and Engineering, Kattankulathur Campus, SRM Institute of Science and Technology.
3B. Baranidharan, Associate Professor, Department of Computer Science and Engineering, Kattankulathur Campus, SRM Institute of Science and Technology.
Manuscript received on January 14, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 849-857 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1112029420/2020©BEIESP | DOI: 10.35940/ijitee.D1112.029420
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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: For an Agro-based country like India where agriculture acts as a main source of livelihood for more than 50% of the population, crop diseases are a major threat to food security. Hence, digital image processing along with proper machine learning algorithms can be utilized for the classification of diseases from the images of a plant. In this paper, a comparative study on the effects of different machine learning models on crop disease prediction has been done. Since Convolutional Neural Network (CNN) proved to be the best for image classification techniques, models based on CNN alone were considered in this study. We compared the performance of small CNN with three pre-trained CNN models namely, Alex Net, ResNet-50, and VGG-16. Small CNN is the CNN model built by us with fewer parameters and suitable for small datasets. The crop tested in this research is Oryza Sativa (Asian Rice) commonly referred to as paddy which is cultivated in abundance in India. The input dataset was fed into the model after performing appropriate pre-processing techniques followed by segmentation. The best accuracy of 66.67% was achieved in the case of ResNet-50 with Adam as the optimizer at a learning rate of 0.0001.
Keywords: CNN, Adam, SGD, RMS Prop, Image Processing, Rice Diseases, Alex Net, ResNet-50, VGG-16.
Scope of the Article: Classification