Plant Disease Prediction

I need a complete, reproducible deep-learning pipeline that takes raw leaf images, learns to recognise plant diseases, and then serves the prediction through a Streamlit interface. Because I do not yet have the images, the first task is to identify and download a suitable, well-labelled dataset from Kaggle. Feel free to compare a few candidates, but the final choice should give good class balance and enough samples per disease category. Once the data is in place, walk through exploratory data analysis, preprocessing, and augmentation inside a Jupyter notebook. From there, build and tune a convolutional neural network (TensorFlow / Keras or PyTorch are both fine) and report the usual metrics plus a confusion matrix so I can judge class-wise performance. When the model is satisfactory, save it and wrap inference in a clean Streamlit app where a user uploads a single image and instantly sees the predicted disease name along with a confidence score. Additionally, integrate AI-based recommendations (e.g., treatment or prevention suggestions), a database (Django / SQLite / PostgreSQL) to store predictions and user uploads, and/or API endpoints so external apps or clients can access the model programmatically. Deliverables Notebook (.ipynb) with data loading, EDA, preprocessing, training, and evaluation [login to view URL] containing the Streamlit interface ready to run with streamlit run [login to view URL] Serialized model file (H5, PT, or SavedModel) [login to view URL] listing every library and version used README explaining setup, training commands, and how to launch the web app The project is finished when the environment from [login to view URL] can run the notebook end-to-end without edits and fire up the Streamlit app to classify a new leaf image successfully. And when I am completely satisfied with the project.

Project ID: 40278726