Deep Neural Network for Disease Detection in Rice Plant Using the Texture and Deep Features

Daniya, T.; Vigneshwari, S.

doi:10.1093/comjnl/bxab022

Cited by 40 publications

(7 citation statements)

References 16 publications

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“…The performance improvement of the developed method is analysed using various existing approaches, like Deep CNN , Multi-level colour image thresholding (Bakar et al, 2018), SVM (Chawal and Panday, 2019), RWW-based neural network (NN) (Daniya and Vigneshwari 2021), RSWbased Deep RNN, Simple CNN (Rahman et al, 2020), DENS-INCEP (Chen et al, 2020), and CNN + SVM (Liang et al, 2019).…”

Section: Competitive Methodsmentioning

confidence: 99%

Rice Plant Leaf Disease Detection and Classification Using Optimization Enabled Deep Learning

Daniya¹,

Vigneshwari²

2023

J ENV INFORM

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An automatic identification and classification of rice diseases are very important in the domain of agriculture. Deep learning (DL) is an effective research area in the identification of agriculture pattern identification where it can effectively resolve the issues of diseases identification. In this paper, a hybrid optimization algorithm is developed to categorize the plant diseases. The pre-processing is made using Region of Interest (ROI) extraction and the input image is created by combining the Rice plant dataset, and Rice disease dataset. The segmentation is accomplished using Deep fuzzy clustering. The features, like statistical features, entropy, Convolutional Neural Network (CNN) features, Local Optimal-Oriented Pattern (LOOP), and Local Gabor XOR Pattern (LGXP) is considered for extracting the appropriate features for further processing. The data augmentation is employed to enlarge the volume of extracted features. Then, the first level classification is made by deep neuro-fuzzy network (DNFN), which is trained using Rider Henry Gas Solubility Optimization (RHGSO) that categories into healthy and unhealthy plants. The RHGSO is the integration of Rider Optimization Algorithm (ROA) and Henry gas solubility optimization (HGSO). After that, second-level classification is made by a Deep residual network (DRN) that is tuned by RHGSO. Thus, the RHGSO-based DRN categorizes the unhealthy plants into Bacterial Leaf Blight (BLB), Blast, and Brown spot. Thus, the implementation of the proposed RHGSO-based deep learning approach offered better accuracy, sensitivity, specificity, and F1-score of 0.9304, 0.9459, 0.8383, and 0.9142.

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Section: Competitive Methodsmentioning

confidence: 99%

Rice Plant Leaf Disease Detection and Classification Using Optimization Enabled Deep Learning

Daniya¹,

Vigneshwari²

2023

J ENV INFORM

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“…They report a grain average classification accuracy of 100% for CNN, 99.95% for DNN, and 99.87% for ANN. In [27], the authors propose the use of the deep recurrent neural network (Deep RNN or DRNN) trained using their proposed RideSpider Water Wave (RSW) and enhanced by integrating the RWW in the spider monkey optimization (SMO). They used a publically available dataset [28], which consists of three classes of diseases, namely bacterial leaf blight 100 images, blast 80 images, and brown spot 96 images.…”

Section: Recent Studiesmentioning

confidence: 99%

Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model

Latif

Abdelhamid

Mallouhy

et al. 2022

Plants

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Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.

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“…Sampaio et al [ 39 ] used artificial neural networks (ANN) for simultaneous and quantitative analysis of paddy quality. As plant diseases cause cereal crop failure, Daniya et al designed techniques for detecting plant diseases using deep recurrent neural networks (Deep RNN), and the experimental results showed the superior performance of RWS-based deep recurrent neural networks [ 40 ]. Ge et al [ 41 ] used long short-term memory networks (LSTM) to predict grain storage temperature and, thus, reduce grain loss.…”

Section: Introductionmentioning

confidence: 99%

FEDformer-Based Paddy Quality Assessment Model Affected by Toxin Change in Different Storage Environments

Zhang

Dong

Liu

et al. 2023

Foods

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The storage environment can significantly impact paddy quality, which is vital to human health. Changes in storage can cause growth of fungi that affects grain quality. This study analyzed grain storage monitoring data from over 20 regions and found that five factors are essential in predicting quality changes during storage. The study combined these factors with the FEDformer (Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting) model and k-medoids algorithm to construct a paddy quality change prediction model and a grading evaluation model, which showed the highest accuracy and lowest error in predicting quality changes during paddy storage. The results emphasize the need for monitoring and controlling the storage environment to preserve grain quality and ensure food safety.

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Deep Neural Network for Disease Detection in Rice Plant Using the Texture and Deep Features

Cited by 40 publications

References 16 publications

Rice Plant Leaf Disease Detection and Classification Using Optimization Enabled Deep Learning

Rice Plant Leaf Disease Detection and Classification Using Optimization Enabled Deep Learning

Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model

FEDformer-Based Paddy Quality Assessment Model Affected by Toxin Change in Different Storage Environments

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