Merge pull request #793 from Shy029/feature/request

Cotton Disease Prediction added

Transfer Learning/Cotton Disease Prediction/README.md

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+# Cotton Disease Prediction with Transfer Learning
+
+This project utilizes deep learning and transfer learning techniques to classify cotton leaf images as either healthy or diseased. By leveraging pre-trained models, it achieves effective disease detection, which can aid in early intervention and improve crop management.
+
+## Project Overview
+
+This cotton disease prediction model uses multiple transfer learning approaches to identify diseases in cotton leaves accurately. The model is based on various deep learning architectures, including ResNet152V2, fine-tuned specifically for this classification task. The notebooks provide detailed steps for data preprocessing, model training, and evaluation.
+
+## Features
+
+- **Disease Classification**: Identifies whether a cotton leaf is healthy or affected by disease.
+- **Transfer Learning**: Utilizes pre-trained models such as ResNet152V2 for enhanced accuracy.
+- **Comprehensive Analysis**: Multiple architectures are tested and compared for the best results.
+
+## Setup and Installation
+
+### Prerequisites
+- Python 3.7 or higher
+- Jupyter Notebook
+
+### Installation Steps
+1. **Clone the Repository**:
+   ```bash
+   git clone https://github.com/yourusername/cotton-disease-prediction.git
+   cd cotton-disease-prediction
+   ```
+
+2. **Install Dependencies**:
+   Install the required packages using `requirements.txt`:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. **Open Jupyter Notebooks**:
+   Start Jupyter Notebook to explore and run each of the transfer learning approaches provided.
+   ```bash
+   jupyter notebook
+   ```
+
+## Project Structure
+
+```plaintext
+/cotton-disease-prediction
+│
+├── Transfer_Learning_ResNet152V2.ipynb  # Notebook for ResNet152V2 model training
+├── Transfer Learning Resnet 50.ipynb       # Notebook for second model approach
+├── Transfer Learning Inception V3.ipynb       # Notebook for third model approach
+└── requirements.txt                     # List of dependencies
+```
+
+## Model Details
+
+The project investigates different transfer learning architectures to classify cotton leaf images, including:
+- **ResNet152V2**: Fine-tuned for cotton disease prediction.
+- **Other Architectures**: Two additional models are explored for comparison.
+- **Evaluation**: Each model is evaluated on accuracy and performance to select the optimal model.

Transfer Learning/Cotton Disease Prediction/requirements.txt

@@ -0,0 +1,104 @@
+absl-py==0.9.0
+astunparse==1.6.3
+attrs==19.3.0
+backcall==0.1.0
+bleach==3.1.5
+cachetools==4.1.0
+certifi==2020.4.5.1
+chardet==3.0.4
+click==7.1.2
+colorama==0.4.3
+cycler==0.10.0
+decorator==4.4.2
+defusedxml==0.6.0
+entrypoints==0.3
+Flask==1.1.2
+Flask-Cors==3.0.8
+gast==0.3.3
+geojson==2.5.0
+google-auth==1.15.0
+google-auth-oauthlib==0.4.1
+google-pasta==0.2.0
+grpcio==1.29.0
+h5py==2.10.0
+idna==2.9
+importlib-metadata==1.6.0
+ipykernel==5.3.0
+ipython==7.14.0
+ipython-genutils==0.2.0
+ipywidgets==7.5.1
+itsdangerous==1.1.0
+jedi==0.17.0
+Jinja2==2.11.2
+joblib==0.15.1
+jsonify==0.5
+jsonschema==3.2.0
+jupyter==1.0.0
+jupyter-client==6.1.3
+jupyter-console==6.1.0
+jupyter-core==4.6.3
+Keras-Preprocessing==1.1.2
+kiwisolver==1.2.0
+lxml==4.5.1
+Markdown==3.2.2
+MarkupSafe==1.1.1
+matplotlib==3.2.1
+mistune==0.8.4
+nbconvert==5.6.1
+nbformat==5.0.6
+notebook==6.0.3
+numpy==1.18.4
+oauthlib==3.1.0
+opencv-python==4.2.0.34
+opt-einsum==3.2.1
+packaging==20.4
+pandas==1.0.3
+pandas-datareader==0.8.1
+pandocfilters==1.4.2
+parso==0.7.0
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==7.1.2
+prometheus-client==0.7.1
+prompt-toolkit==3.0.5
+protobuf==3.8.0
+ptyprocess==0.6.0
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+Pygments==2.6.1
+pyparsing==2.4.7
+pyrsistent==0.16.0
+PySocks==1.7.1
+python-dateutil==2.8.1
+pytz==2020.1
+pywinpty==0.5.7
+pyzmq==19.0.1
+qtconsole==4.7.4
+QtPy==1.9.0
+requests==2.23.0
+requests-oauthlib==1.3.0
+rsa==4.0
+scikit-learn==0.23.1
+scipy==1.4.1
+seaborn==0.10.1
+Send2Trash==1.5.0
+six==1.15.0
+sklearn==0.0
+tensorboard==2.2.1
+tensorboard-plugin-wit==1.6.0.post3
+tensorflow==2.2.0
+tensorflow-estimator==2.2.0
+termcolor==1.1.0
+terminado==0.8.3
+testpath==0.4.4
+threadpoolctl==2.0.0
+tornado==6.0.4
+traitlets==4.3.3
+urllib3==1.25.9
+wcwidth==0.1.9
+webencodings==0.5.1
+Werkzeug==1.0.1
+widgetsnbextension==3.5.1
+wincertstore==0.2
+wrapt==1.12.1
+zipp==3.1.0