Deep Transfer Learning Based Rice Plant Disease Detection Model

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%.

Circle Fitting Based Image Segmentation and Multi-Scale Block Local Binary Pattern Based Distinction of Ring Rot and Anthracnose on Apple Fruits

“…Deep learning has been applied to plant disease image recognition ( Tan et al, 2015 ; DeChant et al, 2017 ; Lu et al, 2017 ; Liu et al, 2018 ; Bansal et al, 2021 ; Caldeira et al, 2021 ; Chen et al, 2021 ; Trivedi et al, 2021 ; Narmadha et al, 2022 ). It can reduce image preprocessing operations and achieve satisfactory disease recognition results.…”

“…Image processing technology has been widely applied in the diagnosis, identification, and monitoring of plant diseases ( Sankaran et al, 2010 ; Barbedo, 2016 ; Vishnoi et al, 2021 ), such as wheat diseases ( Li et al, 2012 ; Johannes et al, 2017 ; Deng et al, 2021 ), maize diseases ( DeChant et al, 2017 ; Chen et al, 2021 ), rice diseases ( Phadikar et al, 2013 ; Lu et al, 2017 ; Narmadha et al, 2022 ), cotton diseases ( Camargo and Smith, 2009 ; Caldeira et al, 2021 ), soybean diseases ( Pires et al, 2016 ; Shrivastava et al, 2017 ; Araujo and Peixoto, 2019 ), cucumber diseases ( Vakilian and Massah, 2013 ; Zhang S. W. et al, 2017 ; Kainat et al, 2021 ), tomato diseases ( Yamamoto et al, 2017 ; Trivedi et al, 2021 ), grape diseases ( Tian et al, 2007 ; Oberti et al, 2014 ; Zhu et al, 2020 ), and citrus diseases ( Pydipati et al, 2006 ; Sankaran et al, 2013 ). Moreover, image processing technology has been used to make disease severity assessments ( Li et al, 2011 ; Barbedo, 2014 ; Vieira et al, 2014 ; Shrivastava et al, 2015 ; Ganthaler et al, 2018 ), conduct pathogen identification ( Chesmore et al, 2003 ; Deng et al, 2012 ; Wang et al, 2021 ), and perform automatic counting of pathogen spores ( Li X. L. et al, 2013 ; Li et al, 2017 ).…”

An Improved Deep Residual Convolutional Neural Network for Plant Leaf Disease Detection

“…ey achieved a classification accuracy of 96.6% on the tomato leaf disease dataset using their model. e authors in [15] used a dense convolutional neural network (DenseNet) and multilayer perceptron for detecting bacterial leaf blight, brown spot, and leaf smut diseases in rice crops. e maximum classification accuracy of the rice disease detection model was 97.68%.…”

An improved deep convolutional neural network for detecting plant leaf diseases

“…In Reference 9, the authors compared the performance of standard machine learning and deep transfer learning techniques in plant leaf disease detection and found the performance of the The literature survey reveals that dense convolutional neural networks perform better than other transfer learning techniques in plant disease detection. 13,17,25 Dense networks create deeper connections between the layers than simple convolutional neural networks, avoid the vanishing-gradient problem and minimize the number of training parameters. Most of the state-of-art transfer learning techniques, however, were trained on ImageNet dataset.…”