Automated In-field Assessment of Strawberry Characteristics for Mechanized Harvesting

L5's proposed automated in-field strawberry characteristics assessor addresses USDA SBIR Topic Area 8.13 Plant Production and Protection specifically improved harvesting of specialty crops and development of cyber-physical systems to support sustainable agriculture. This research and development will create a key component of L5's automated strawberry harvester a system that can alleviate the labor shortages currently hindering the industry. Strawberries are a major U.S. specialty crop with harvesting labor comprising 39% of grower operating expenses. Mechanized harvesting is vital to keep strawberry production viable in the United States. A major component of any minimally viable product that is a drop-in replacement for human labor is the ability to rapidly and accurately assess four characteristics of strawberries: ripeness damage shape and size. Based on these a pick decision can be made and once picked categorized for retail sale juicing or discard. This research aims to implement algorithms to estimate the four characteristics strictly from images using a mix of classical deep learning and geometric vision techniques. A custom dataset of several hundred strawberries will be gathered from an operational farm with characteristics labeled via human input and 3-D scans. Iterations of a field-ready assessor will be prototyped to determine best practices and estimate operational throughput. The anticipated result is a cyber-physical system which assesses characteristics in the field and meshes with the workflow of L5's automated harvester. If successful this technology will ensure that harvest quality from a mechanized platform surpasses the status quo unlocking the $1.8B market for harvest labor.