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- W3157087401 abstract "Every year apple yield has been affected by Black rot and Cedar apple rust. It has a significant effect on both the apple industry and the country's economy. Here, we recommend a system to detect diseases from the infected apple leaves by combining machine learning and image processing principles. This approach can classify both infected and non-infected apple leaves efficiently. The identification is started by preprocessing the image using several image processing techniques, including the Otsu thresholding algorithm and histogram equalization. Using the image segmentation region of the infected part separates, and a Multiclass SVM recognizes the disease type from the original leaf image among 500 images with 96% accuracy. It also demonstrates the percentage of the total infected area of that diseased apple leaf image." @default.
- W3157087401 created "2021-05-10" @default.
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- W3157087401 date "2021-01-05" @default.
- W3157087401 modified "2023-10-16" @default.
- W3157087401 title "Prediction of Apple Leaf Diseases Using Multiclass Support Vector Machine" @default.
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- W3157087401 doi "https://doi.org/10.1109/icrest51555.2021.9331132" @default.
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