A novel method to improve computational and classification performance of rice plant disease identification

Archana, K. S.; Srinivasan, S.; Bharathi, S.; Balamurugan, R.; Prabakar, T.N.; Britto, A. Sagai Francis

doi:10.1007/s11227-021-04245-x

Cited by 20 publications

(6 citation statements)

References 26 publications

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“…The CBAM module (Woo et al, 2018) is added after each RI‐Block to identify small intra‐class differences and large inter‐class differences (Zhao, Sun, et al, 2022). Archana et al (Archana et al, 2022) proposed a novel support vector machine‐based probabilistic neural network (NSVMBPNN) which gives better classification accuracy than models like Probabilistic Neural Network (PNN), Naïve Bayes, and SVM.…”

Section: Methodsmentioning

confidence: 99%

“…Syed-Ab-Rahman et al utilized ResNet101 (He et al, 2017) for feature extraction and Region Proposal Networks (RPN) (Ren et al, 2016) et al, 2018), Support Vector Machine (SVM) (Cortes & Vapnik, 1995), and ResNet50 (Simonyan & Zisserman, 2015), evaluated on tomato plant images to detect nine diseases (Altalak et al, 2022) et al, 2018) is added after each RI-Block to identify small intra-class differences and large inter-class differences (Zhao, Sun, et al, 2022). Archana et al (Archana et al, 2022) proposed a novel support vector machine-based probabilistic neural network (NSVMBPNN) which gives better classification accuracy than models like Probabilistic Neural Network (PNN), Naïve Bayes, and SVM.…”

Section: Technique Based On Hybrid Approachmentioning

confidence: 99%

“…In Reference Syed‐Ab‐Rahman et al (2022), ResNet101 is used for feature extraction, and Region Proposal Networks (RPN) are used to extract the potential ROIs. In Reference Archana et al (2022), the NIBCFE approach is proposed to extract colour features; BPF and GLCM techniques are utilized to extract texture features of the image; and for finding the diameter and area of the infected region, shape features are extracted. Figure 4 shows the various feature extraction techniques adopted by researchers in their model's development.…”

Section: Steps Used For Detection and Classification Of Plant Diseasementioning

confidence: 99%

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An analysis to investigate plant disease identification based on machine learning techniques

Duhan,

Gulia,

Gill

et al. 2024

Expert Systems

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In agriculture, crops are severely affected by illnesses, which reduce their production every year. The detection of plant diseases during their initial stages is critical and thus needs to be addressed. Researchers have been making significant progress in the development of automatic plant disease recognition techniques through the utilization of machine learning (ML), image processing, and deep learning (DL). This study analyses the recent advancements made by researchers in the field of ML techniques for identifying plant diseases. This study also examines various methods used by researchers to produce ML solutions, such as image preprocessing, segmentation, and feature extraction. This study highlights the challenges encountered while creating plant disease identification systems, such as small datasets, image capture conditions, and the generalizability of the models, and discusses possible solutions to cater to these problems. Still, the development of a solution that automatically detects various plant diseases for various plant species remains a big challenge. To address these challenges, there is a need to create a system that is trained on an extensive dataset that contains images of various types of diseases a plant can suffer from, and plant images should be taken at various stages of the disease's development. This study further presents an analysis of various methods used at different stages of plant disease identification.

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Section: Methodsmentioning

confidence: 99%

Section: Technique Based On Hybrid Approachmentioning

confidence: 99%

Section: Steps Used For Detection and Classification Of Plant Diseasementioning

confidence: 99%

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An analysis to investigate plant disease identification based on machine learning techniques

Duhan,

Gulia,

Gill

et al. 2024

Expert Systems

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“…Further, this process generally leads to inaccurate classification outcomes which limit the yield of rice in the previous decades. Therefore, for crop diseases identification image processing approaches are exploited [8]. The image processing stages involved for identifying diseases in crop involves image acquisition, pre-processing, segmenting, feature extracting and classifying.…”

Section: Introductionmentioning

confidence: 99%

Infectious diseases of Rice plants classified using a deep learning-powered Least Squares Support Vector Machine Model

Goluguri¹,

Devi²,

CH³

2022

INDJCSE

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In the field of agriculture, identifying and classifying Oryza Sativa diseases is always a hot research topic. Almost in all the states of India, Oryza Sativa (rice) is cultivated as a necessary food crop. However, rice plants are highly caused due to diseases that drastically affect the agricultural sectors. Researchers are searching for solutions for reliable and exact detection approaches for crop leaf diseases. A Oryza Sativa plant leaf disease classification system that is modelled relying on the concept of deep learning-driven Least Square Support Vector Machine is investigated in the present study. This work focus on the Oryza Sativa plant leaf diseases like Brown Leaf spot(BS), Bacterial leaf blight (BLB), Leaf smut(LS), Tungro(LT), and Leaf Blast(LB). To create a contrast-enhanced image dataset, the input Oryza Sativa plant disease image dataset is resized as well as histogram equalization and the Kuwahara filter are applied. The diseased part is segmented using spatial fuzzy clustering (SFC). Then, a multi-model LeNet5 is used in extracting features, and the least square support vector machine(LSSVM) is employed to classify the Oryza Sativa plant leaf diseases images. Further, to improve the accuracy of the disease classification Weighted Sparrow Search Optimization (WSSO) is utilized. The proposed multi-model LSSVM-WSSO outperformed the existing models on the two benchmark datasets when evaluated on the terms of accuracy, precision, recall, and F1-measure values.

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“…A method for plant leaf disease recognition as well as classification using K-nearest neighbor (KNN) classifier has been proposed [36]. The texture features are extracted from the leaf disease images for classification [37]. K-NN classifier was used to classify Alternaria, anthracnose's, bacterial blight, leaf spots, and cankers of different plant species.…”

Section: ░ 1 Introductionmentioning

confidence: 99%

Linear Vector Quantization for the Diagnosis of Ground Bud Necrosis Virus in Tomato

Kadam

Dhumale²,

Dhumale

et al. 2022

IJEER

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In this varying environment, a correct and appropriate disease diagnosis including early preclusion has never been more significant. Our study on disease identification of groundnut originated by Groundnut Bud Necrosis Virus will cover the way to the effective use of image processing approach in agriculture. The difficulty of capable plant disease protection is very much linked to the problems of sustainable agriculture and climate change. Due to the fast advancement of Artificial Intelligence, the work in this paper is primarily focused on applying Pattern Recognition based techniques. The purpose is to determine the grade of disease to control by developing a model for the selection of bud blight disease caused by GBNV in tomatoes. The images are classified according to the grade of the disease. Different methods have been applied to make a proper diagnosis by bringing clarity in the diagnostic results. Linear Vector Quantization works well than, Radial Basis Function, Back Propagation Neural Network and Support Vector Machine.

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A novel method to improve computational and classification performance of rice plant disease identification

Cited by 20 publications

References 26 publications

An analysis to investigate plant disease identification based on machine learning techniques

An analysis to investigate plant disease identification based on machine learning techniques

Infectious diseases of Rice plants classified using a deep learning-powered Least Squares Support Vector Machine Model

Linear Vector Quantization for the Diagnosis of Ground Bud Necrosis Virus in Tomato

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