Plant leaf infection detection Project

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.

Plant leaf infection detection Project

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

Training Process

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.

See also  50+ Image Processing Computer Vision Projects Source Code

Test Process

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 vtupulse@gmail.com

Summary:

Here you find the Plant leaf infection detection Project using Machine Learning and Image processing Project for final year students. If you like the material share it with your friends. Like the Facebook page for regular updates and YouTube channel for video tutorials.

Leave a Comment

Your email address will not be published. Required fields are marked *