CROPCARE AI

Inspiration

Farmers often detect crop diseases and pest infestations only after visible damage has spread, leading to severe yield loss, excessive pesticide use, and financial stress. Manual inspection is slow, subjective, and hard to scale across large farms. Most existing tools only classify diseases without measuring severity, predicting risk, or offering clear action steps. We built CropCare AI to transform visual inspection into automated, actionable crop health management.

What it does

CropCare AI is a vision-first crop health monitoring system powered by OpenCV 5 and AWS cloud architecture. The system executes a structured 6-step loop: Detect → Assess → Predict → Recommend → Monitor → Verify.

Detection & Segmentation: OpenCV 5 preprocessing (CLAHE, HSV filtering, ROI extraction) and YOLO/CNN models isolate diseased leaf areas.
Severity Estimation: Quantifies affected tissue area percentage to estimate infection severity.
Risk Prediction & Action: Blends vision insights with local weather and crop data to predict near-term risk and generate immediate mitigation plans.

How we built it

OpenCV 5 DNN Module & Image Processing: High-performance image enhancement, contour analysis, and real-time vision inference.
AWS Cloud Infrastructure: Hosted on AWS EC2 (Graviton-optimized) for scalable OpenCV image processing pipelines, AWS S3 for leaf image storage, and Amazon DynamoDB for real-time risk logs.
Backend & Frontend: Python backend integrated with lightweight web dashboards for mobile and desktop access.

Challenges we faced

Handling uneven lighting, background noise, and shadow variations in field-captured images. We solved this using OpenCV 5 advanced CLAHE and dynamic thresholding.
Optimizing deep learning models to run low-latency inference on cloud instances without losing segmentation precision.

Accomplishments that we're proud of

Successfully built an end-to-end perception-to-decision pipeline (Agentic Vision workflow).
Accurate real-time leaf lesion extraction and severity calculation using OpenCV 5 algorithms.

What we learned

Deep hands-on experience using OpenCV 5 DNN module alongside AWS cloud compute services.
Translating complex raw computer vision output (bounding boxes/masks) into actionable metrics for non-technical users.

What's next for CropCare AI

Integrating multi-spectral drone video feed analysis using OpenCV streaming modules.
Deploying edge-device monitoring stations for direct offline farm integration.