Jeff Chima Maduka

# GreenDoc — AI-Powered Crop Disease Detection ## What I Built GreenDoc is a mobile-first web application that lets any farmer take a photo of a diseased crop leaf and receive an instant AI diagnosis — the disease name, severity level, and exact treatment recommendation. ## The Problem Nigeria loses $3.6 billion annually to agricultural losses. Crop diseases go undetected until it's too late. The nearest agricultural extension officer is 40km away. ## Tech Stack - **Model:** EfficientNetB0 with transfer learning (PyTorch) - **Dataset:** 35,000+ images across 19 disease classes - **Backend:** FastAPI deployed on Railway - **Frontend:** React + Tailwind CSS deployed on Vercel - **Model Format:** ONNX for optimized inference ## Model Performance - Round 1: 95.79% validation accuracy - Round 2: 99.43% validation accuracy, 100% F1 score on all 19 classes ## Code Example ```python model = models.efficientnet_b0(weights="IMAGENET1K_V1") for param in model.parameters(): param.requires_grad = False model.classifier[1] = nn.Linear( model.classifier[1].in_features, 19 ) ``` ## Results - 19 crop disease classes detected - 99.43% validation accuracy after fine-tuning - Live on mobile at https://cropdoc-frontend-ldld.vercel.app ## Challenges - Domain gap between lab training images and real farm photos - ONNX model size limitations on free hosting - Confident misclassifications on Mosaic Virus and Septoria ## What's Next - Retrain with real field condition images - Google login and Paystack payments - Android APK for full offline use