Tomato Leaf Disease Detection
(2024)AI-Powered Crop Health Monitoring
Duration: 8 weeks
Synopsis
Custom YOLO-11M object detection model for automated identification of 7 distinct tomato leaf diseases. Implemented Fourier-based regularization for sharper disease localization.
The Making Of
🎯 The Challenge
Manual crop inspection is time-consuming and error-prone. Need to identify 7 distinct leaf states including Bacterial Spot, Early Blight, and Late Blight with precise localization.
💡 The Solution
Built custom YOLO-11M architecture with C2f Fusion Blocks (25% parameter reduction), SPPF for multi-scale pooling, and Polarized Self-Attention. Innovated with Fourier Loss term using 2D FFT to preserve high-frequency edge details.
🚀 The Impact
Fourier-enhanced model improved Box Precision by 2.4% and mAP@50 by 2.4% over baseline with zero inference overhead. Model focuses on disease textures rather than being distracted by shadows or dirt.
Key Metrics
Tech Stack (Cast & Crew)
- YOLO-11MObject Detection
- PyTorchML Framework
- FFTFourier Loss
- PythonImplementation
Quick Info
- Year
- 2024
- Duration
- 8 weeks
- Complexity
- Senior
- Featured
- Yes