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Deep Transfer Learning Based Rice Plant Disease Detection Model

R. P. Narmadha1,*, N. Sengottaiyan2, R. J. Kavitha3

1 Department of Computer Science and Engineering, Anna University, Chennai, 600025, India
2 Department of Computer Science and Engineering, Sri Shanmugha College of Engineering and Technology, Sankari, Salem, 637304, India
3 Department of ECE, University College of Engineering, Panruti (A Constituent College of Anna University), Panruti, 607106, India

* Corresponding Author: R. P. Narmadha. Email: email

Intelligent Automation & Soft Computing 2022, 31(2), 1257-1271. https://doi.org/10.32604/iasc.2022.020679

Abstract

In agriculture, plant diseases are mainly accountable for reduction in productivity and leads to huge economic loss. Rice is the essential food crop in Asian countries and it gets easily affected by different kinds of diseases. Because of the advent of computer vision and deep learning (DL) techniques, the rice plant diseases can be detected and reduce the burden of the farmers to save the crops. To achieve this, a new DL based rice plant disease diagnosis is developed using Densely Convolution Neural Network (DenseNet) with multilayer perceptron (MLP), called DenseNet169-MLP. The proposed model aims to classify the rice plant disease into three classes namely Bacterial Leaf Blight, Brown Spot, and Leaf Smut. Initially, preprocessing takes place in three levels namely channel separation, grayscale conversion, and noise removal using median filtering (MF). Then, the fuzzy c-means (FCM) based segmentation process identifies the diseased portion in the rice plant image. The pretrained DenseNet169 technique is used as a feature extractor and the final layer is replaced by the MLP to perform rice plant disease classification. The effectiveness of the proposed model has been validated against benchmark dataset and the simulation outcome is examined under diverse measures. The obtained results defined the superior results of the DenseNet169-MLP model over the recently presented methods with the maximum accuracy of 97.68%.

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APA Style
Narmadha, R.P., Sengottaiyan, N., Kavitha, R.J. (2022). Deep transfer learning based rice plant disease detection model. Intelligent Automation & Soft Computing, 31(2), 1257-1271. https://doi.org/10.32604/iasc.2022.020679
Vancouver Style
Narmadha RP, Sengottaiyan N, Kavitha RJ. Deep transfer learning based rice plant disease detection model. Intell Automat Soft Comput . 2022;31(2):1257-1271 https://doi.org/10.32604/iasc.2022.020679
IEEE Style
R.P. Narmadha, N. Sengottaiyan, and R.J. Kavitha "Deep Transfer Learning Based Rice Plant Disease Detection Model," Intell. Automat. Soft Comput. , vol. 31, no. 2, pp. 1257-1271. 2022. https://doi.org/10.32604/iasc.2022.020679



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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