About LeafScan AI
An end-to-end deep learning system for plant disease detection, built as a portfolio project demonstrating production ML engineering.
Dataset
Trained on approximately 87,867 RGB images of healthy and diseased crop leaves, categorized into 38 classes. Based on the PlantVillage dataset.
87,867
Total Images
38
Disease Classes
70/15/15%
Train/Val/Test
Supported Crops
14 crop types with multiple disease states each.
AppleCornTomatoPotatoGrapePeachPepperStrawberryBlueberrySoybeanRaspberrySquashCherryOrange
Model Architecture
- ✓3× Convolutional blocks (32 → 64 → 128 filters) with MaxPooling
- ✓Dropout (0.3) for regularization
- ✓Dense(256, ReLU) → Dense(38, Softmax)
- ✓Optimizer: Adam · Loss: Categorical cross-entropy
- ✓Input: 128×128×3 RGB, normalized to [0, 1]
- ✓~95% test accuracy
Author
Vijaya Suhaas Nadukooru
Built as a portfolio project to demonstrate end-to-end ML engineering — from data preprocessing and model training to FastAPI deployment and a modern React frontend.