AI Disease & Pest Detection
AI-assisted analysis of crop images to flag potential disease and pest conditions. Coconut is the initial crop focus, with further validation planned.
SKYLAB360 · AI for Agriculture
Agri-Vision Pro
AI-Powered Early Disease Detection & Precision Agriculture Drone Platform
Agri-Vision Pro is being developed to help identify crop disease and pest conditions earlier using AI-assisted image analysis. The platform combines crop imaging, computer vision, crop-health assessment and drone-enabled workflows, with a roadmap toward targeted intervention.
AI dashboard prototype shown in SKYLAB360’s Agri-Vision Pro project presentation.
From Crop Images to Actionable Intelligence
Agri-Vision Pro focuses on plant-level crop-health information—not land surveying or mapping as a service.
Plant-Level Crop Health
Computer-vision workflows designed to support crop-health assessment and more informed decisions at plant level.
Drone-Enabled Crop Care
Drone platform integration and autonomous navigation are part of the development pathway. Precision spraying remains under development and validation.
How the Intelligent Workflow Works
The project roadmap connects crop observation with AI-supported decisions and future precision intervention.
01
Scanning
Capture crop and canopy images for analysis.
02
Detection
AI-assisted identification of potential pest and disease conditions.
03
Analysis
Assess crop-health signals through the AI processing workflow.
04
Decision
Support AI-assisted mission planning and follow-up decisions.
05
Intervention
Targeted precision intervention is a development-stage capability.
Project Progress So Far
Figures below are reported in SKYLAB360’s Agri-Vision Pro presentation and describe project progress, not independently certified performance.
100,000+Coconut-tree images tested
600+Farmers engaged
12,000+Coconut trees represented in the AI training work, as reported in the project materials
60%Current detection accuracy reported in the presentation; 90%+ is the target, not a current result
The presentation also reports field activity across Pattambi and Chittur in Palakkad district, coconut-canopy navigation trials using a DJI Tello, and an AI dashboard prototype that flagged a Mandari infection. Results should be validated on representative field data before commercial performance claims are made.
Initial Crop Focus & Expansion
Coconut is the starting point. Multi-crop validation is a planned next stage.
Coconut
Initial focus for AI-assisted disease and pest detection.
Paddy
Planned disease and pest detection validation.
Coffee
Planned canopy-health and ripeness assessment.
Rubber
Planned tapping-panel disease detection focus.
Tea is also identified in the project materials as a future crop for leaf-blight and pest monitoring.
Designed for Practical Field Conditions
The platform roadmap combines AI, computer vision and onboard processing to support agricultural workflows in varied field conditions. Specific functionality depends on development, integration and validation progress.
- AI-assisted crop monitoring and disease detection
- Edge AI and a workflow designed for low-connectivity environments
- Autonomous flight and navigation integration
- AI-assisted mission planning and crop-health reporting
- Targeted precision spraying under development
Building the Future of Autonomous Agriculture
Interested in field trials, research collaboration, pilot deployments or Agri-Vision Pro? Contact SKYLAB360 to discuss your crop, use case and partnership requirements.
