Sugarcane Leaf Disease Detection

[IkkiOcean]

Title : Sugarcane Leaf Disease Detection

Aim : This project focuses on classifying sugarcane leaf diseases using different machine learning models. The dataset contains images of sugarcane leaves classified into various categories. Image preprocessing, data augmentation, and ensemble learning methods are applied to boost the model’s accuracy.

Closes: #144

Describe the add-ons or changes you've made 📃

• Added a Jupyter notebook implementing an ensemble model for sugarcane disease prediction.
• Combined multiple deep learning architectures: MobileNet, InceptionV3, VGG16, Conv2D, and ResNet152 into a cohesive model.
• Implemented data preprocessing steps, including image augmentation and normalization.
• Conducted model training and evaluation, achieving an overall accuracy of 90.94%.
• Included detailed comments and explanations throughout the notebook for clarity and reproducibility.
• Visualized model performance metrics and comparison graphs for individual models.

Model Evaluation

Model	Accuracy
MobileNet	83.20%
VGG16	79.76%
Conv2D	86.06%
ResNet152	81.21%
InceptionV3	86.06%
Ensemble	90.94%

Type of change ☑️

• My code follows the code style of this project.
• New feature (non-breaking change which adds functionality)

Checklist: ☑️

• My code follows the code style of this project.
• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

Please assign level 3 as I have created 5-6 Deep learning models , trained them and have evaluated the accuracy.

[github-actions]

Thank you for submitting your pull request! 🙌 We'll review it as soon as possible. In the meantime, please ensure that your changes align with our CONTRIBUTING.md. If there are any specific instructions or feedback regarding your PR, we'll provide them here. Thanks again for your contribution! 😊

[IkkiOcean]

@UppuluriKalyani Please mark this PR LEVEL 3. I have created 5-6 Deep Learning model , have trained them(which almost took 12 hours ) and have evaluated them on the basis of accuracy. I even created an ensemble model to increase the total accuracy of the project.