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Classification of Rice Diseases using Convolutional Neural Network Models
https://orcid.org/http://orcid.org/0000-0002-7858-4267
Automatic diagnosis and control of rice plant disease are highly desired by agricultural experts. Many machine learning approaches have been proposed in automating rice disease identification, where deep learning has generated significant outcomes. In the present study, state-of-the-art deep learning models based on transfer learning approach are deployed for the classification of various disease symptoms in rice plant images. The efficiency of the leading pre-trained VGG-16 and GoogleNet convolutional neural network (CNN) models on the held-out dataset is evaluated using a threefold cross-validation method. The trained VGG-16 and GoogleNet CNN models achieved an average classification accuracy of 92.24% and 91.28%, respectively. The experimental results demonstrate the practical usefulness of utilizing the deep learning methodology employing 12,000 labeled images of three different rice diseases with 24 different symptoms. The proposed work finds applications in on-field identification of rice disease symptoms providing actionable information to farmers and policy makers in many aspects of crop handling and management practices.
Classification of Rice Diseases using Convolutional Neural Network Models
https://orcid.org/http://orcid.org/0000-0002-7858-4267
Automatic diagnosis and control of rice plant disease are highly desired by agricultural experts. Many machine learning approaches have been proposed in automating rice disease identification, where deep learning has generated significant outcomes. In the present study, state-of-the-art deep learning models based on transfer learning approach are deployed for the classification of various disease symptoms in rice plant images. The efficiency of the leading pre-trained VGG-16 and GoogleNet convolutional neural network (CNN) models on the held-out dataset is evaluated using a threefold cross-validation method. The trained VGG-16 and GoogleNet CNN models achieved an average classification accuracy of 92.24% and 91.28%, respectively. The experimental results demonstrate the practical usefulness of utilizing the deep learning methodology employing 12,000 labeled images of three different rice diseases with 24 different symptoms. The proposed work finds applications in on-field identification of rice disease symptoms providing actionable information to farmers and policy makers in many aspects of crop handling and management practices.
Classification of Rice Diseases using Convolutional Neural Network Models
https://orcid.org/http://orcid.org/0000-0002-7858-4267
Automatic diagnosis and control of rice plant disease are highly desired by agricultural experts. Many machine learning approaches have been proposed in automating rice disease identification, where deep learning has generated significant outcomes. In the present study, state-of-the-art deep learning models based on transfer learning approach are deployed for the classification of various disease symptoms in rice plant images. The efficiency of the leading pre-trained VGG-16 and GoogleNet convolutional neural network (CNN) models on the held-out dataset is evaluated using a threefold cross-validation method. The trained VGG-16 and GoogleNet CNN models achieved an average classification accuracy of 92.24% and 91.28%, respectively. The experimental results demonstrate the practical usefulness of utilizing the deep learning methodology employing 12,000 labeled images of three different rice diseases with 24 different symptoms. The proposed work finds applications in on-field identification of rice disease symptoms providing actionable information to farmers and policy makers in many aspects of crop handling and management practices.
Classification of Rice Diseases using Convolutional Neural Network Models
J. Inst. Eng. India Ser. B
Yakkundimath, Rajesh (author) / Saunshi, Girish (author) / Anami, Basavaraj (author) / Palaiah, Surendra (author)
Journal of The Institution of Engineers (India): Series B ; 103 ; 1047-1059
2022-08-01
13 pages
Article (Journal)
Electronic Resource
English
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