Developed an advanced drone imagery analysis system to empower pineapple farmers in Africa with actionable insights that optimize crop management and increase yields.
🚀 Core Contributions
🧠 Agricultural Computer Vision System
Multi-label Detection Engine: Designed and trained a custom CNN model to identify pineapple fruits, flowers, and non-flowering plants
Production Forecasting: Implemented accurate fruit counting algorithms to predict harvest volumes
Precision Agriculture: Created systems to identify non-flowering plants for targeted flowering induction treatments
Large-scale Image Processing: Developed efficient algorithms for analyzing high-resolution drone imagery
📊 Data Pipeline & Preprocessing
Custom Dataset Creation: Built comprehensive dataset with 3,186 labeled images across 3 classes
Image Enhancement: Implemented advanced preprocessing techniques including noise removal and color calibration
Sliding Window Architecture: Created optimized sliding window approach for high-resolution drone imagery
Geometric Calibration: Designed systems to handle perspective distortion in aerial imagery
🔧 Model Development & Optimization
Transfer Learning: Fine-tuned Faster R-CNN ResNet101 architecture for agricultural applications
Extensive Training: Conducted 160,000 epochs of training to achieve optimal performance
Cross-validation: Implemented rigorous testing using 30% held-out dataset to ensure generalization
Performance Optimization: Utilized CUDA and cuDNN for accelerated model training and inference