An efficient approach for automated system to identify the rice crop disease using intensity level based multi-fractal dimension and twin support vector machine

Chaudhary, Shashank; Kumar, Upendra

doi:10.1080/03235408.2023.2222444

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…Another crucial aspect of this technique is to keep track of the plant's progression, which is needed in order to detect and identify the various symptoms that are invisible to most observers. Here is the baseline method where the GLCM and ILMFD feature extraction methods were used individually with different classification methods [25]. The scope of this work that was done before was expanded to include the new features.…”

Section: Methodsmentioning

confidence: 99%

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Automated Detection and Classification of Rice Crop Diseases Using Advanced Image Processing and Machine Learning Techniques

Chaudhary,

Kumar

2024

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Agriculture, a pivotal sector in the Indian economy, plays a crucial role in national development. A significant challenge within this domain is the detection of crop diseases, with brown spot, leaf blast, and bacterial blight being prevalent afflictions in rice crops. This study presents an innovative approach, integrating Gray-level Co-occurrence Matrix (GLCM) and Intensity-Level Based Multi-Fractal Dimension (ILMFD) for feature extraction in disease identification. The efficacy of this integrated technique was evaluated through a comparison with various classifiers. Specifically, the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Neuro-Genetic Algorithm (Neuro-GA) were employed to ascertain their precision in disease detection. It was observed that the combination of GLCM and ILMFD with the Neuro-GA classifier achieved an accuracy exceeding 90%. Remarkably, when paired with the SVM classifier, this integrated approach yielded a precise accuracy of 96.7% in detecting brown spot disease in rice. These findings not only validate the effectiveness of the GLCM and ILMFD methods in feature extraction but also highlight the superior performance of the SVM classifier in crop disease detection. This research contributes significantly to the field, offering a robust solution for accurate disease diagnosis in rice crops, thereby aiding in the sustainable management of agricultural practices.

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Section: Methodsmentioning

confidence: 99%

“…To achieve the highest accuracy for rice diseases, the two feature extraction methods used were the GLCM and ILMFD with three different classifiers, namely the ANN, SVM, and Twin support vector machine (TWSVM) [25]. The paper presents a framework that includes the use of various extraction methods.…”

Section: Literature Workmentioning

confidence: 99%

Automated Detection and Classification of Rice Crop Diseases Using Advanced Image Processing and Machine Learning Techniques

Chaudhary,

Kumar

2024

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Identification of rice crop diseases using gray level co-occurrence matrix (GLCM) and Neuro-GA classifier

Chaudhary,

kumar

2024

Int J Syst Assur Eng Manag

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Evaluation and Categorisation of Hispa Rice Disease Severity Levels Using CNN-RF Model

Yadav,

Kumar,

Das

et al. 2024

2024 International Conference on Automation and Computation (AUTOCOM)

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An efficient approach for automated system to identify the rice crop disease using intensity level based multi-fractal dimension and twin support vector machine

Cited by 3 publications

References 52 publications

Automated Detection and Classification of Rice Crop Diseases Using Advanced Image Processing and Machine Learning Techniques

Automated Detection and Classification of Rice Crop Diseases Using Advanced Image Processing and Machine Learning Techniques

Identification of rice crop diseases using gray level co-occurrence matrix (GLCM) and Neuro-GA classifier

Evaluation and Categorisation of Hispa Rice Disease Severity Levels Using CNN-RF Model

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