Rice Leaf Disease Prediction System using Deep Learning Flask Web App

Demo Video
Rice Leaf Disease Identification is a deep learning project aimed at detecting and classifying diseases in rice leaves using computer vision techniques. The project utilizes transfer learning with the ResNet152V2 model, incorporating an attention layer to focus on important parts of the image. The dataset used for training the model contains approximately 2600 images belonging to six different classes.
Dataset
The dataset used for training the model is sourced from Kaggle and contains images of rice leaves affected by different diseases. You can access the dataset
here
Model Training
The deep learning model is trained using transfer learning techniques with the ResNet152V2 architecture. Transfer learning allows leveraging pre-trained models on large datasets and fine-tuning them for specific tasks. In this project, the ResNet152V2 model is fine-tuned on the rice leaf disease dataset to classify different types of diseases.
Classes
The model is trained to classify rice leaf images into the following disease classes:
Healthy
Brown spot
Leaf blast
Bacterial leaf blight
Leaf scald
Narrow brown spot
Deployment
The trained model is deployed using Flask, a lightweight Python web framework. It accepts images of rice leaves as input and identifies the disease present in the leaf.
How to Install
To use the deployed model:
Download Code.
Install the required dependencies specified in the
requirements.txt
file.
Run the Flask application.
Upload an image of a rice leaf to the application.
The application will predict the disease present in the leaf and display the result.

Detailed Project Description

Rice Leaf Disease Prediction System using Deep Learning Flask Web App applies machine learning to a real dataset: it prepares the data, trains and evaluates a model, and exposes the trained model through a simple interface so predictions can be made on new inputs.

How It Works
1. The dataset is collected/loaded, cleaned and explored (missing values, encoding, scaling).
2. Features are engineered and the data split into training and test sets.
3. One or more models are trained and compared using accuracy, precision/recall, F1 or RMSE.
4. The best model is saved and served through a web/GUI form that returns predictions.

Technology Roles
- Python
- Flask
- Deep Learning

Implementation Steps
1. Collect the dataset (public repository or own data) and understand each attribute.
2. Set up the environment (Python, Flask, Deep Learning) and load the data into a notebook.
3. Clean the data: handle missing values, outliers, encoding and scaling; visualise distributions.
4. Split into train/validation/test sets and engineer relevant features.
5. Train several candidate models, tune hyper-parameters and compare metrics.
6. Save the best model and wrap it in a simple web/GUI form for live predictions.
7. Document results with plots, confusion matrix / error charts and conclusions.

Key Features
- Data pre-processing pipeline
- Model comparison
- Evaluation metrics and plots
- Prediction interface

Expected Output
On completion the student will have evaluation results (metrics, confusion matrix or error plots) and a working prediction form.

Deliverables
- Notebook / source code with comments
- Cleaned dataset and trained model file
- Evaluation report with metrics and plots
- Prediction interface