Optimised hybrid classification approach for rice leaf disease prediction with proposed texture features

Sridevi, S.; Kumar, K. Suresh

doi:10.1080/23307706.2022.2141359

Cited by 4 publications

(3 citation statements)

References 65 publications

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“…These extracted features are then fed into a classification system consisting of multilayer perceptron (MLP) and long short-term memory (LSTM) networks. Finally, the model produces predicted outcomes for rice disease detection (Sridevi and Kiran Kumar, 2022).…”

Section: Literature Surveymentioning

confidence: 99%

OSPS-MicroNet: a distilled knowledge micro-CNN network for detecting rice diseases

Tharani Pavithra,

Baranidharan

2024

Front. Comput. Sci.

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More than half of the world's population relies on rice as their primary food source. In India, it is a dominant cereal crop that plays a significant role in the national economy, contributing to almost 17% of the GDP and engaging 60% of the population. Still, the agricultural sector faces numerous challenges, including diseases that can cause significant losses. Convolutional neural networks (CNNs) have proven effective in identifying rice diseases based on visual characteristics. However, CNNs require millions of parameters, resulting in high computational complexity, so deploying these models on limited-resource devices can be difficult due to their computational complexity. In this research, a lightweight CNN model named Oryza Sativa Pathosis Spotter (OSPS)-MicroNet is proposed. OSPS-MicroNet is inspired by the teacher-student knowledge distillation mechanism. The experimental results demonstrate that OSPS-MicroNet achieves an accuracy of 92.02% with only 0.7% of the network size of the heavyweight model, RESNET152. This research aims to create a more streamlined and resource-efficient model to detect rice diseases while minimizing demands on computational resources.

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Section: Literature Surveymentioning

confidence: 99%

OSPS-MicroNet: a distilled knowledge micro-CNN network for detecting rice diseases

Tharani Pavithra,

Baranidharan

2024

Front. Comput. Sci.

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“…Table II compares EC (Bi-GRU, CNN, and DMN) + OLIHFA-BA with existing classifiers such as DBN, RNN, QNN, SVM, RF, LSTM, ICRMBOS [45], TL-DCNN [46], and LMBWO [47]. The proposed EC (Bi-GRU, CNN, and DMN) + OLIHFA-BA model has produced superior results compared to differentiated approaches for optimization and classification models.…”

Section: B Performance Analysismentioning

confidence: 99%

Optimal Training Ensemble of Classifiers for Classification of Rice Leaf Disease

Sridevi¹,

Kumar²

2023

IJACSA

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Rice is one of the most extensively cultivated crops in India. Leaf diseases can have a significant impact on the productivity and quality of a rice crop. Since it has a direct impact on the economy and food security, the detection of rice leaf diseases is the most important factor. The most prevalent diseases affecting rice leaves are leaf blast, brown spots, and hispa. To address this issue, this research builds a new classification model for rice leaf diseases. The model begins with a preprocessing step that employs the Median Filter (MF) process. Improved BIRCH is then utilized for picture segmentation. Features such as LBP, GLCM, color, shape, and modified Median Binary Pattern (MBP) are retrieved from segmented images. Then, an ensemble of three classification models, including Bi-GRU, Convolutional Neural Network (CNN), and Deep Maxout (DMN) is utilized. By adjusting the model weights, the suggested Opposition Learning Integrated Hybrid Feedback Artificial and Butterfly algorithm (OLIHFA-BA) will train the model to improve the performance of the proposed work.

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“…Sridevi et al [11] introduced a deep learning-based metaheuristic methodology for paddy leaf disease detection and identification that significantly enhances accuracy, generality, and training performance. For this investigation, they used field images of many kinds of paddy leaf diseases.…”

Section: Related Workmentioning

confidence: 99%

ATRLeNet: A Deep Learning Model for Enhanced Classification of Oryza Sativa Pathologies

Goluguri,

Gogula

et al. 2023

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Diseases affecting the Oryza Sativa (rice) plant result in substantial agricultural losses, leading to a decline in crop productivity by up to 25% and posing a significant threat to global food security. Hence, the rapid and accurate diagnosis of such diseases is paramount to ensure effective treatment and to enhance overall plant health. This has led to an increased interest among plant pathologists in developing reliable methods for identifying diseases in Oryza Sativa crops. In this study, an innovative disease classification model for the Oryza Sativa plant is proposed, leveraging the Optimal Adaptive Boosting Cascade Classifier (OABCC) and the efficient-artificial fish swarm optimization (EAFSO). A weighted image fusion technique is utilized in the pre-processing stage for image denoising, combining the outcomes of homomorphic filtering (HAF), Laplace filtering (LAF), and the Kuwahara Filter (KF). The diseased portions of the Oryza Sativa plant leaf are localized using the OABCC, while Soft Non-Maximum Suppression (SN-MS) is deployed to select the optimal detection box for each item. The LeNet model, bolstered with an atrous-convolution layer, is integrated into the OABCC for improved disease classification. Further enhancement in model accuracy is achieved through the application of the EAFSO optimization strategy. When applied to the OABCC-ATRLeNet model for rice disease classification, the EAFSO optimization strategy outperforms other strategies such as WSSO, CSO, AFSO, and PSO. This research underscores the potential of deep learning approaches for robust and accurate classification of plant diseases, contributing significantly to the efforts in securing global food resources.

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Optimised hybrid classification approach for rice leaf disease prediction with proposed texture features

Cited by 4 publications

References 65 publications

OSPS-MicroNet: a distilled knowledge micro-CNN network for detecting rice diseases

OSPS-MicroNet: a distilled knowledge micro-CNN network for detecting rice diseases

Optimal Training Ensemble of Classifiers for Classification of Rice Leaf Disease

ATRLeNet: A Deep Learning Model for Enhanced Classification of Oryza Sativa Pathologies

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