Leaf Diseases Detection

This project uses a deep learning model to classify leaf diseases. The model is trained on a dataset of leaf images labeled with three classes: Healthy, Powdery, and Rust.

Dataset Used

The dataset used in this project was taken from Kaggle and was filtered and processed for the specific usecases of this project.

Technologies Used

1. Keras
2. TensorFlow
3. Matplotlib
4. Numpy

Running the Code

To run the code, execute the main.py script. This script loads a pre-trained model: SproutIQ_Disease_Detection_Model.keras, evaluates it on the validation set, and makes predictions on the test set. The predictions are then plotted and displayed.

Model Evaluation

The model's performance is evaluated in terms of validation loss and validation accuracy, which are printed to the console.

Making Predictions

The script makes predictions on the first batch of images from the test set. It plots the images along with their true labels and predicted labels.

Future Work

The project includes commented-out code for building a Kivy app. This is a future direction for the project, allowing users to interact with the model in a graphical user interface on mobile devices.