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- W4285295532 abstract "The manual identification of plant leaf diseases by experts using visual samples is a laborious task ridden with issues of accuracy and efficacy. The goal of an automatic approach is to address the concerns of time, effort, and accuracy. The presented work explores machine learning algorithms for disease identification in different plant leaves. Some state-of-the-art methods are used for pre-processing. The performance of the “artificial neural network (ANN)” model is compared with “Naive Bayes (NB), K-Nearest Neighbors (KNN), and decision tree (DT).” The experiments are carried out on benchmark datasets Kaggle and Mendeley. The histogram equalization and binary thresholding are applied for pre-processing of input leaf images. Further, for abnormality segmentation, seeded region growing (SRG) algorithm is also applied. “Graylevel Co-occurrence Matrix (GLCM)” is used for feature extraction. The performance of the proposed plant leaves disease detection model has exhibited superior performance measured in “accuracy, sensitivity, specificity, and precision, FPR, FNR, NPV, FDR, F1 score, and MCC.”" @default.
- W4285295532 created "2022-07-14" @default.
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- W4285295532 date "2022-01-01" @default.
- W4285295532 modified "2023-10-16" @default.
- W4285295532 title "Disease Detection of Plant Leaves with the Aid of Region Growing and Neural Network: A Comparative Analysis" @default.
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- W4285295532 doi "https://doi.org/10.1007/978-981-19-0840-8_21" @default.
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