operations will be applied to the images to remove any artifacts, noise, or

unwanted structures. The pre$-$processing techniques will be applied to both

healthy and diseased plant images.

Feature Extraction: In this milestone, features such as color, texture, and

shape will be extracted from the pre$-$processed images. These features will

be used to differentiate between healthy plants and plants infected with

diseases.

Color features will be extracted using color histograms, which will represent the

color distribution of the images. Texture features will be extracted using

techniques, which will capture the texture properties of the images. Shape

features will be extracted using techniques such as contour detection, which

will capture the shape properties of the images.

Classification: In this milestone, the extracted features will be used to

classify the images into different categories such as healthy or diseased.

Different classification algorithms such as support vector machines, decision

trees, and random forests will be implemented and evaluated.

Classification algorithms will be trained on the extracted features to learn the

patterns in the images and differentiate between healthy and diseased plants. The

algorithms will be evaluated based on their accuracy, precision, recall, and F$1$

score.

Evaluation of the System: The performance of the developed system will be

evaluated using various metrics such as precision, recall, and F$1$ score.

These metrics will be calculated by comparing the results of the automated

system with manual diagnosis.

The performance of the system will be evaluated on a set of test images that were

not used during the training and validation phases. The results of the automated

system will be compared to the manual diagnosis to evaluate the accuracy of the

system.Overview:

Cotton is one of the most important crops worldwide and is affected by many

diseases that can cause yield loss and quality reduction. In recent years, an

increasing interest has been in developing automated systems for detecting

cotton diseases using image$-$processing techniques. This project aims to create

an automated system for detecting cotton diseases using image processing

techniques

Milestones:

Collection and Pre$-$processing of Images: In this milestone, images of

healthy plants and plants infected with diseases will be collected and pre$-$

processed to remove noise, enhance contrast, and improve the quality of

the image. Techniques such as histogram equalization, morphological

operations, and contrast stretching will be applied to the images.

Image pre$-$processing techniques will be used to remove noise and enhance the

quality of the images. Techniques such as smoothing, filtering, and morphological

please implement this program in matlab, I need to check my work