The startup develops an agricultural intelligence platform that employs artificial intelligence to predict pest and disease threats on a per-plot basis and assess crop emergence for optimal growth. By providing real-time plant stand counts and insights into yield health, the platform enhances decision-making for farmers, improving crop management and sustainability.
Funding
$21.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Farmers face challenges in accurately detecting and managing pest and disease outbreaks, assessing plant health, and predicting yields, leading to potential crop losses and inefficient resource allocation. Traditional scouting methods are time-consuming, labor-intensive, and often lack the precision needed for effective intervention.
Solution
AgroScout offers an agricultural intelligence platform that leverages AI and aerial imagery to provide farmers with real-time insights into crop health and potential threats. The platform uses imagery captured from smartphones, off-the-shelf drones, and satellites to deliver per-plot diagnostics of pests and diseases, plant stand counts, canopy coverage analysis, and high-resolution orthomosaic imagery. By integrating these data layers, AgroScout enables early detection of issues, optimized resource allocation, and improved decision-making throughout the growing season. The platform's AI-driven analysis, combined with agronomist verification, ensures accurate and actionable reports, empowering farmers to protect their investments and achieve sustainability goals.
Target Audience
AgroScout serves farmers, growers, agricultural processors, and supply chain managers seeking to improve crop yields, reduce operational costs, and enhance sustainability through data-driven insights.
Features
- AI-powered pest and disease detection with field-wide monitoring down to the leaf level
- Automated plant stand count for emergence evaluation and yield prediction
- Canopy coverage analysis using Leaf Area Index (LAI) to inform chemical application and irrigation decisions
- High-resolution orthomosaic imagery (1 cm/pixel) for a complete field overview and identification of stress indicators
- Satellite monitoring with NDVI and RGB imagery for assessing crop vigor and detecting early stress
- Mobile app for data capture and field navigation with geo-tagged photos
- Supply chain alerts and reports for proactive risk management