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A leaf image localization based algorithm for different crops disease classification

Information Processing in Agriculture 2022年第3期9卷 456-474页

作者： Yashwant Kurmi suchi Gangwar ECE Department Maulana Azad National Institute of TechnologyBhopalIndia Agriculture Department RKDF UniversityBhopalIndia

Agricultural crop production is a major contributing element to any country’s *** maintain the economic growth of any country plants disease detection is a leading factor in *** contribution of the proposed algorithm is to optimize the extracted infor-mation from the available resources for the betterment of the result without any additional *** proposed technique basically localizes the leaf region prior to the image classification into healthy and *** novelty of this work is to fuse the information extracted from the available resources and optimize it to enhance the expected *** leaf colors are analyzed using color transformation for the seed region *** mapping of a low-dimensional RGB color image into L*a*b color space provides an expansion of the spectral *** neighboring pixels-based leaf region growing is applied on the initial *** order to refine the leaf boundary and the disease-affected areas,we employed a random sample consensus(RANsAC)for suitable curve *** feature sets using bag of visual words,Fisher vectors,and handcrafted features are extracted followed by classification using logistic regression,multilayer perceptron model,and support vector *** performance of the proposal is analyzed through PlantVillage datasets of apple,bell pepper,cherry,corn,grape,potato,and *** simulation-based analysis of the proposed contextualization-based image categorization process outperforms as compared with the state of *** proposed approach provides average accuracy and area under the curve of 0.932 and 0.903,respectively.

关键词： image segmentation and classification Computer-aided diagnosis crop’s leaf image Tomato leaf image localization

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