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Plant leaf infection detection Project for final year students
In this project machine learning and image processing techniques are used to detect and classify the plant leafs as infectious and non-infectious.
The project is tested on the different plant leaf datasets such as Bell Pepper, Maize and tomato leaf datasets.
Follwo the link to download the dataset for the project: Click here to download the dataset
The following are the types in the raw dataset:
Color Image: Original RGB images
Grayscale Image: grayscaled version of the raw images
Segmented Image: RGB images with just the leaf segmented and color corrected.
Execution steps of the Plant leaf infection detection Project
At the end of the text file such as ‘bacterial_result.txt’, the average infection percentage for that particular disease for the selected leaf type will be mentioned.
After creating training text file for each training folder, now we are ready for testing the proposed model.
At the end of the text file such as ‘healthy_test_result.txt’, the classification accuracy for that particular disease for the selected leaf type will be mentioned.
Source Code of Plant leaf infection detection Project using Machine Learning and Image processing
Source code will be given on request, you can send your details to firstname.lastname@example.org
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