Open Access

ARTICLE

Deep Transfer Learning Based Rice Plant Disease Detection Model

R. P. Narmadha^1,*, 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:

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

Received 02 June 2021; Accepted 08 July 2021; Issue published 22 September 2021

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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Keywords

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