intelinair
Airbus to Provide Imagery Services That Enable Intelinair's Crop Analytics Platform
High-resolution imagery services from the Pléiades and SPOT 6/7 satellites will provide 50cm and 1.5m geospatial imagery into the AGMRI platform as an additional input, so that farmers and ag retailers can make data-driven decisions and proactively manage their operations with confidence. "We are very pleased to be providing high-resolution imagery from Pléiades and SPOT 6/7 satellites, whose complementarity makes them the ideal assets for enriched crop management. They deliver fresh information along with historical insights to help drive decisions in the field. Our data along with powerful machine learning and computer vision from Intelinair will help improve crop yields," said François Lombard, Director of the Intelligence business at Airbus Defense and Space. With satellite and other aerial imagery sources, farmers receive a new perspective of the agronomic conditions in the form of emergence, plant health, weed detection and harvest readiness in their fields throughout the growing season. From this perspective, product performance and crop damage issues become visible so timely management decisions can be made to protect yields and optimize financial returns.
- North America > United States > Virginia > Fairfax County > Herndon (0.06)
- North America > United States > Indiana > Marion County > Indianapolis (0.06)
- Food & Agriculture > Agriculture (1.00)
- Aerospace & Defense (0.99)
- Information Technology > Artificial Intelligence (1.00)
- Information Technology > Data Science > Data Mining (0.42)
Residue Density Segmentation for Monitoring and Optimizing Tillage Practices
Hobbs, Jennifer, Dozier, Ivan, Hovakimyan, Naira
"No-till" and cover cropping are often identified as the leading simple, best management practices for carbon sequestration in agriculture. However, the root of the problem is more complex, with the potential benefits of these approaches depending on numerous factors including a field's soil type(s), topography, and management history. Instead of using computer vision approaches to simply classify a field as till vs. no-till, we instead seek to identify the degree of residue coverage across a field through a probabilistic deep learning segmentation approach to enable more accurate analysis of carbon holding potential and realization. This approach will not only provide more precise insights into currently implemented practices, but also enable a more accurate identification process of fields with the greatest potential for adopting new practices to significantly impact carbon sequestration in agriculture.
- Energy (0.94)
- Food & Agriculture > Agriculture (0.92)