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\nDeep Learning Soil Classification\n

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\nThe objective of this case study is to train and test a Convolutional Neural Network to detect soil types from images. Deep Learning (DL) is a subset of Machine Learning that uses Neural Network inspired architecture to make predictions....\nThe objective of this case study is to train and test a Convolutional Neural Network to detect soil types from images. Deep Learning (DL) is a subset of Machine Learning that uses Neural Network inspired architecture to make predictions. Convolutional Neural Networks (CNN) are a type of DL model that is effective in learning patterns in 2-dimensional data such as images. Images of soils are used to train a classifier to identify common soil types and fractions of soil type.

This exercise demonstrates the use of image classification to distinguish between soil types. Although applied to soil, the same methods and code can be used for any type or number of items. This example can be modified by including train and test photos in folders that are named with the item type. The code automatically takes the name of the folder as the photo label for training the classifier. Additional examples of image-based CNNs are found in the Bit and Crack Classification Case Study.
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Resource Details

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Educator Rating
Not yet Rated
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Media Length
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28:50
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Grade
10th - Higher Ed
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Subjects
STEM
3 more...
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Media Type
Instructional Videos
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Source:
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\nAPMonitor.com\n\n
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Date
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2022
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Language
English
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Audiences
For Teacher Use
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Usage Permissions
Fine Print: Educational Use
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Concepts

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classification, range
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Additional Tags

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deep learning, convolutional neural network, tensorflow, machine learning, course, online, train, validation, convolutional layer, dense layer, convolutional network, training set, image dimension, list directory, texture classification, test directory, classification list, zip file, save directory, test divided, sequential model, true type, testing data, directory, dense, model, python, save, percentage, divided, image, layers, import, photo, true
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Classroom Considerations

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Best For: Explaining a topic
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