Titlebook: Computer Vision and Machine Learning in Agriculture; Mohammad Shorif Uddin,Jagdish Chand Bansal Book 2021 The Editor(s) (if applicable) an

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,Spring Web Flow’s Architecture,ing strategies. A dataset is generated using 1574 images of various diseases. This dataset is expanded to 7870 images through the data augmentation technique by utilizing scaling and rotation. Experimentation is performed by dividing the data into training and testing categories at a ratio of 8:2. T

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https://doi.org/10.1007/978-1-4302-1625-4segmentation, it is important to determine and find an optimal technique for a particular context. For an automated machine vision-based fruit disease recognition context, image segmentation plays a very important role for extracting features from the location and size of defective areas. In this re

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The Definitive Guide to Spring Web Flowen made using different computer vision techniques to address different problems of agriculture. The machine vision-based diagnosis of fruits and vegetables is a notable problem domain in this regard. This problem domain has beckoned the computer vision and machine learning researchers to contribute

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978-981-33-6426-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

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,Spring Web Flow’s Architecture,uation, we have collected data from various online sources that included leaf images of six plants, including tomato, potato, rice, corn, grape, and apple. In our investigation, we implement numerous popular convolutional neural network (CNN) architectures. The experimental results validate that the

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Detection of Rotten Fruits and Vegetables Using Deep Learning,

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