SBIR Phase II: Autonomous harvesting, mapping, and forecasting for fresh produce through application of robotics, computer vision, and machine learning

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is significant in the areas of robotic applications, precision agriculture, deep learning, environment sensing, and co-robots. Automated harvesting enables the ability to grow healthy and sustainable food, and the long-term market opportunity for agricultural robotics is $150+ B. This project will develop a robotic solution for large “controlled environment” farms, such as glasshouses and outdoor polytunnels. These use capital and labor more intensively but require less water (~90% reduction), chemical use (~50-70% reduction), and fertilizer use (~50% reduction). Robots could reduce the required labor and enable competitive operations with lower impact on the environment. This Small Business Innovation Research (SBIR) Phase II project will advance the fields of computer vision/machine learning and robotics controls by solving frontier problems faced when operating in highly dynamic, precision-requiring biological environments like farms. By evolving and combining approaches from the forefront of computer vision, the project will develop a novel approach to temporospatial tracking of specific fruit as it moves and changes over time, gathering data of unprecedented detail on plant life cycle. Using that plant-level database, the project will integrate visual data to test and refine detection and modeling of berry ripeness. Lastly, the project will integrate in-field spectrometry into ripeness classification. These objectives will underpin a novel precision agriculture solution. 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.