Advanced Growing Resources Inc.

Sbir Phase I: Novel Handheld Spectroscopy for the Early Detection of Crop Afflictions -this Small Business Innovation Research (SBIR) Phase I Project Will Focus on Development of a Handheld Optical Scanner That Integrates Precise Imaging With Optical Spectroscopy. Although This Technology Has Broad Applications Ranging From Industrial Quality Control to Education, the Beachhead Sector to Be Addressed Is Precision Agriculture. to Feed a Growing Population of 10 Billion in 2050, Agricultural Production Will Need to Increase by 70% Over the Next 30 Years. Critical to This Mission Is the Development of Innovative Tools and Strategies for Crop Protection and Health Management to Preserve the World?s Food Supply. the Potential Customers in This Segment Are Crop Growers, Consultants, and Field Scouts Aiming to Act Early While Reducing the Use of Harmful and Costly Chemicals. the Global Crop Monitoring Market Was Estimated at $2 Billion in 2019 and Is Anticipated to Reach $6 Billion by 2027, Growing at 15.3% Annually. the Sensing and Imaging Segment Accounted for 49.3% of the Market in 2019. This Technology Has the Potential to Support the Adoption of More Sustainable Agricultural Practices as Well as the Economic Viability of Small- to Medium-scale Farm Operations in the U.s. by Providing an Accessible and Affordable Tool for Disease Detection and Crop Health Management. the Intellectual Merit of This Project Is the Commercial Development of Novel and Affordable Imaging Spectroscopy Technology in Addition to Application-specific Analytics for the Early Detection of Crop Afflictions. Optical Components of the Scanning Device That Will Be Designed-for-manufacturing in This Project Bridge the Current Gap Created by Systems That Trade the Detection Capabilities of Spectral Resolution for Spatial Resolution. the Scanner Will Be Deployed to Collect Data on Grapes, a Key, High-value Crop That Is Susceptible to a Number of Afflictions That Destroy Yields Before the Human Eye Can Detect Them. Machine Learning Algorithms Will Be Developed From This Data to Detect Stressed and Diseased States in the Plant Before They Become Apparent to Visual Inspection. These Models Will Then Be Validated in the Field With User Feedback From Horticultural Experts and Customers. Successful Implementation of This Technology Will Facilitate the Detection of Crop Diseases Before They Spread. This Detection Capability Can Increase Yields While Reducing the Use of Costly and Environmentally-harmful Pesticides and Fertilizers. This Award Reflects NSF'S Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.