Automated Crop Scouting with Deep Vision
Client Partner: Ceres Agritech Global · Build Timeline: 6 Months
Project Tech Stack
Allocated Engineering Pod
- Computer Vision Engineersx2
- Full-Stack Developersx3
- Product Managerx1
"Crocus Robotics' AI integration transformed our scouting workflow. What used to take two weeks of manual footwork is now flagged instantly on our screens."
Dr. Amanda Vance
VP of Agronomy, Ceres Agritech
1. Context & Business Goal
The Core Challenge: Ceres struggled with manual, slow scouting methods to check for crop pests and diseases across 2.5 million hectares, leading to delayed treatments and 18% average yield loss.
The Agreed Objective: Automate disease detection from drone and tractor cameras using deep neural networks, reducing scouting cycles from weeks to minutes.
2. Technical Research & Discovery
Analyzed over 450,000 crop images in collaboration with agronomists, identifying specific leaf spots, fungal signatures, and insect patterns across wheat, corn, and soy. Prior to writing any code, our team compiled reference data frameworks and mapped API endpoints to identify speed limits.
3. System Architecture Design
Layout Scheme: Custom map-based dashboard highlighting hotspots with color-coded alerts (Red = Critical, Orange = Warning, Green = Healthy) and offline mobile inspection screens for field workers.
Database Infrastructure Setup: Edge processing on tractor cameras compiled using ONNX, syncing geo-tagged disease nodes via MQTT to an AWS centralized PostgreSQL and Pinecone grid.
4. Production Implementation Lifecycle
Data Gathering & Cleaning
Labled crop images with disease bounding boxes using agronomist supervision.
Model Training & Optimization
Trained custom YOLOv8 and ResNet architectures, optimizing using TensorRT for edge hardware.
Infrastructure Pipeline Integration
Setup MQTT message queues to process incoming telemetry from active tractors.
5. Validated Business Outcomes
- ✓Early Detection Efficiency: Flagged rust fungus infections 10 days before standard scouting routines, saving over $4.2M in potential crop loss.
- ✓Saved $1.8M in pesticide costs through localized spraying patterns.
- ✓Offered active maps across 100% of farmed areas within 3 months of launch.