Two YOLO-based computer vision projects — detecting nutrient deficiencies in coffee leaves and monitoring livestock behavior from camera feeds.
This project trained a YOLOv5 object detection model to identify nutrient deficiencies in coffee crop leaves from field photographs. Given that visual symptoms of nitrogen, phosphorus, and potassium deficiency overlap significantly, the model needed to distinguish subtle spectral and morphological differences between classes.
The project was built during the UIUC AI Foundry Agriculture Track, combining agronomic domain knowledge with computer vision techniques to create a field-deployable detection tool.
✓ Completed# Train YOLOv5 on coffee leaf deficiency dataset # After cloning ultralytics/yolov5 and setting up Roboflow export import subprocess subprocess.run([ 'python', 'train.py', '--img', '640', '--batch', '16', '--epochs', '50', '--data', 'coffee-deficiency/data.yaml', '--weights', 'yolov5s.pt', # pretrained COCO weights '--name', 'coffee_npk_v1', '--cache' ]) # Classes: ['N_deficiency', 'P_deficiency', 'K_deficiency'] # Annotated using Roboflow with bounding boxes
Sample images showing the model's bounding box detections on coffee leaves — each box labeled with deficiency class and confidence score.
An ongoing computer vision research project applying YOLO-based object detection and pose estimation to monitor cattle in farm environments. The system detects individual cows, tracks their movement, and classifies feeding behavior — enabling automated livestock health and productivity monitoring.
The system uses a multi-module approach: cow face detection for individual identification, body keypoint estimation for posture analysis, and a feeder classification module currently in development.
⟳ Ongoing ResearchManual livestock monitoring is labor-intensive and misses early signs of illness or distress. An automated vision system can flag abnormal behaviors — prolonged lying, reduced feeding activity, unusual postures — that often precede health decline, enabling early veterinary intervention at scale.
In Nepal's hill farming context, where herds are often managed by small families, even a simple camera + Raspberry Pi deployment could meaningfully reduce livestock losses. This project explores the feasibility of that kind of low-cost precision livestock farming.