[Proposing new issue]: Sugarcane_Leaf_Disease_Detection

[Vasudha-01]

:red_circle: Project Title: Sugarcane Leaf Disease Detection

:red_circle: Aim: To develop a machine learning model capable of classifying sugarcane leaves into three categories: Healthy, Red Rot, and Red Rust, utilizing image processing techniques and machine learning algorithms.

:red_circle: Dataset:

Healthy leaves: 75 Images Red Rot leaves: 74 Images Red Rust leaves: 75 Images Total Images: 224 Images

Exploratory Data Analysis (EDA): Visualize the distribution of images across different categories. Display sample images from each category. Preprocess the images (resizing, sharpening, etc.).

Algorithms to Implement and Compare: DenseNet201 with SVM-inspired approach: Use transfer learning with DenseNet201 pre-trained on ImageNet. Custom classification head with Dense and Dropout layers. Squared hinge loss function for SVM-inspired classification.

Convolutional Neural Network (CNN): Build a CNN from scratch. Use standard layers such as Conv2D, MaxPooling2D, Flatten, Dense, and Dropout.

Random Forest: Extract features from images using pre-trained models. Train a Random Forest classifier on the extracted features.

Support Vector Machine (SVM): Extract features from images using pre-trained models. Train an SVM classifier on the extracted features.

Model Evaluation:

Compare the models using accuracy scores and other relevant metrics. Use confusion matrices and classification reports for detailed performance analysis. Implementation Steps:

Preprocess the dataset (resizing, sharpening). Perform EDA. Implement and train the models. Evaluate and compare the models. Save the best-performing model. 📍 Follow the Guidelines to Contribute in the Project: You need to create a separate folder named as the Project Title. Inside that folder, there will be four main components. Images - To store the required images. Dataset - To store the dataset or, information/source about the dataset. Model - To store the machine learning model you've created using the dataset. requirements.txt - This file will contain the required packages/libraries to run the project on other machines. Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions. :red_circle::yellow_circle: Points to Note:

The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR. "Issue Title" and "PR Title should be the same. Include issue number along with it. Follow Contributing Guidelines & Code of Conduct before starting to contribute. :white_check_mark: To be Mentioned while taking the issue:

Full name: Vasudha J GitHub Profile Link: https://github.com/Vasudha-01 Participant ID (If not, then put NA): Approach for this Project: What is your participant role? VSoC, Contributor Happy Contributing 🚀

All the best. Enjoy your open-source journey ahead. 😎

[github-actions[bot]]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[abhisheks008]

Hi @Vasudha-01 thanks for creating the issue but this repository mainly focuses on machine learning models instead of deep learning models. Hence this issue can not be worked on here.

Closing this issue as not working.