Analysis of Methods of Machine Learning Techniques for Detection and Classification of Brown Spot (Rice) Disease

Chaudhary, Shashank; kumar, Upendra

doi:10.13189/ujar.2022.100502

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…The study covers the research outcomes using CNN and SVM for classifying rice disease. The accuracy of CNNs at identifying and measuring rice diseases was 95%, while that of SVM was 82% [24].…”

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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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: 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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“…Examining specific papers reveals the strategic use of established datasets like PlantVillage and Kaggle Rice Disease, as seen in studies by Refs. [ 8 , 57 ], respectively, underlining the importance of leveraging well-established resources. The classification diversity, spanning from 2 to 10 classes, underscores the nuanced and intricate nature of the various rice diseases studied.…”

Section: Literature Reviewmentioning

confidence: 99%

Advancements in rice disease detection through convolutional neural networks: A comprehensive review

Gülmez

2024

Heliyon

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Systematic Review on Machine Learning and Computer Vision Methodology for Precision Agriculture Applications

KUAN,

Goh,

Lim

2024

Preprint

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Analysis of Methods of Machine Learning Techniques for Detection and Classification of Brown Spot (Rice) Disease

Cited by 3 publications

References 16 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

Advancements in rice disease detection through convolutional neural networks: A comprehensive review

Systematic Review on Machine Learning and Computer Vision Methodology for Precision Agriculture Applications

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