sahilali09/rice-leaf-disease-detection

Rice Leaf Disease Detection - PRCP-1001

Capstone Project: Complete Jupyter Notebook

This repository contains a comprehensive Jupyter notebook for the PRCP-1001 Rice Leaf Disease Detection project.

What's Included

Single Jupyter Notebook (Rice_Leaf_Disease_Detection.ipynb) covering:

1. Task 1: Complete Data Analysis Report

   • Dataset overview (119 images, 3 classes)
   • Class distribution visualization
   • Image properties analysis (dimensions, intensity, channels)
   • Color channel analysis (RGB distributions)
   • Texture and edge analysis (Sobel filters, histograms)
   • Data quality assessment

2. Task 2: Model Building

   • Custom CNN (from scratch with BatchNorm, Dropout)
   • VGG16 Transfer Learning (with VGG-specific preprocessing: BGR mean subtraction)
   • ResNet50 Transfer Learning (with ResNet-specific preprocessing: zero-centering)
   • MobileNetV2 Transfer Learning (with MobileNet-specific preprocessing: [-1,1] scaling)
   • EfficientNetB0 Transfer Learning (with EfficientNet-specific preprocessing: mean/std normalization)
   • Each model includes: frozen phase + fine-tuning phase

3. Task 3: Data Augmentation Analysis Report

   • Visual demonstration of 10 augmentation techniques
   • Combined augmentation pipeline (rotation, shift, shear, zoom, flip, brightness)
   • Rationale and impact analysis

4. Model Comparison Report

   • Side-by-side comparison of 9 model configurations
   • Accuracy, F1-Score, Precision, Recall, Training Time
   • Confusion matrices for all models
   • Classification reports
   • Visual bar chart comparisons

5. Challenges Faced & Techniques Used Report

   • Small dataset challenge → Augmentation + Transfer Learning
   • Overfitting → Dropout, EarlyStopping, BatchNorm
   • Variable dimensions → Standardized resizing to 224x224
   • Preprocessing compatibility → Model-specific preprocess_input
   • Class imbalance → Stratified splits
   • Gradient issues → BatchNormalization
   • Learning rate selection → ReduceLROnPlateau
   • Fine-tuning strategy → Progressive unfreezing
   • Background noise → Random crops/shifts

Dataset

• Source: PRCP-1001 RiceLeaf.zip
• Classes: Bacterial leaf blight (40), Brown spot (40), Leaf smut (39)
• Total: 119 images
• Split: 60% Train / 20% Validation / 20% Test (stratified)

Preprocessing Note

Each transfer learning model uses its own dedicated preprocessing pipeline (applied inside the Keras model graph via preprocess_input):

Model	Preprocessing
VGG16	BGR + ImageNet mean subtraction
ResNet50	Zero-centering (÷255 - mean)
MobileNetV2	Scale to [-1, 1] (÷127.5 - 1)
EfficientNetB0	Mean/std normalization per channel

How to Use

1. Download the dataset from the provided link
2. Extract to ./Data/ with subfolders for each class
3. Run all cells sequentially in the notebook
4. Results (plots, JSON, classification reports) are generated inline

Requirements

tensorflow, numpy, pandas, matplotlib, seaborn, opencv-python, scikit-learn, scikit-image

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Submitted as part of PRCP-1001 Capstone Project

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