AGR develops spectral sensing engines integrated with tractor-mounted cameras and edge-AI for early agricultural diagnostics. This technology identifies fungal, pest, viral, nutrient, and water issues in crops up to one week sooner than traditional methods. The system provides high-accuracy data for real-time spray reduction or prescription mapping for sprayer OEMs and custom applicators.
Funding
Funding not disclosed
+14Founders
Product
Problem
In industries like agriculture and food processing, current quality assessment methods often rely on manual inspection or lab testing, which are time-consuming, costly, and prone to human error. Traditional spectroscopy equipment is often bulky and requires specialized expertise, making real-time, on-site analysis challenging.
Solution
AGR develops the Spectre MINI, a compact, plug-and-play spectroscopy module that integrates edge AI for real-time quality assessment. The device enables users to rapidly and accurately determine product composition and quality directly in the field or on the production line. By embedding AI intelligence into process control, the Spectre MINI facilitates faster training and execution compared to image-based systems, leading to more accurate results. This technology allows for early detection of diseases and nutrient deficiencies in agriculture, as well as on-plant fruit ripeness evaluation, ultimately reducing waste and enhancing operational efficiency.
Target Audience
The primary target audience includes professionals in agriculture, food processing, and other heavy industries who need rapid, accurate, and on-site quality assessment of materials and products. This includes sprayer OEMs, custom applicators, and farms.
Features
- Compact, plug-and-play spectroscopy module designed for edge-AI and industrial integration
- Real-time quality assessment of materials and products
- AI models tailored for specific applications, such as agriculture, forestry, and food & beverage
- Rapid spectral analysis for early disease and nutrient detection in plants
- On-plant fruit ripeness evaluation
- Integration with existing process control systems
- Faster training and execution compared to image-based systems