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The code can be executed as jupyter notebook or also on Google Collab.
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The dataset from Kaggle should be downloaded as a zip file and the whole folder called "PlantVillage" should be included as a zip file. https://www.kaggle.com/datasets/faysalmiah1721758/potato-dataset
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Make sure that the code has "!unzip /content/PlantVillage.zip" to use the zip file as dataset in Google Collab.
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The training takes about 50 to 60 min to finish 13 epochs on Google Collab, after which you start getting the output, results and visualizations.
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The predictions of leaves that are done using this code has been restricted to 9 leaves only for saving time but the code can be modified to check for all leaves.
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Software solution where a Convolutional Neural Network (CNN) model is deployed to classify leaf images into categories of early blight, "late blight," or healthy." The backend architecture incorporates the CNN model.
DhruvJoshi003/Potato_Disease_Classification
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Software solution where a Convolutional Neural Network (CNN) model is deployed to classify leaf images into categories of early blight, "late blight," or healthy." The backend architecture incorporates the CNN model.
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