SBIR Phase II: Orchestration of Multiple Robotic Subsystems into a Commercially Viable Robotic Strawberry Harvesting System

该项目的更广泛的影响/商业潜力包括开发浆果采摘后筛选和处理，将提供一种安全和有效的方法，将受污染的浆果从自动化收割机的加工流中移除。该项目将推进机器人收割的机器视觉技术，并将是一项关键的使能技术，导致人们接受自动收割是安全和有效的。该项目涉及的市场部门是草莓种植者，尽管该技术也将适用于其他类型的水果和蔬菜种植。该项目先进的自动化收获技术将缓解草莓种植者面临的长期和日益严重的劳动力短缺，并将确保草莓仍然负担得起，消费者可以获得。填补农业劳动力短缺带来的需求是一个10亿美元的商业机会。这个小型企业创新研究(SBIR)第二阶段项目将开发新的视觉处理和检查方法，这对使用自动化、机器人草莓收割机至关重要。草莓农民是否接受自动收获技术取决于收割机能否从植株中去除不良浆果，而不允许不良浆果进入收割机包装流，并可能污染大量浆果。为了实现这一点，收割机必须在从自动采摘到最终包装的多个阶段识别和消除病态、腐烂、损坏或被侵扰的浆果。识别要淘汰的浆果的分类方法将非常准确，具有非常高的检测率和低的误警率。所开发的方法将适用于安装在受恶劣室外环境以及机器人收获装置上的冲击和振动环境影响的农业机械上。将开发新的处理流程，允许对整个浆果进行自动检查，而不会损坏水果或造成受感染浆果的交叉污染风险，显著推动自动化草莓加工的最新水平。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this project includes development of berry post-picking screening and handling that will provide a safe and efficient way to remove contaminated berries from the processing stream of an automated harvester. The project will advance machine vision technology for robotic harvesting and will be a key enabling technology leading to acceptance of automated harvesting as safe and effective. The market sector addressed by this project is strawberry farmers, though the technology will be applicable to other types of fruit and vegetable farming as well. The automated harvesting technology advanced by this project will alleviate chronic and worsening labor shortages faced by strawberry farmers and will ensure that strawberries remain affordable and available to consumers. Filling the need created by farming labor shortages is a $1 billion business opportunity.This Small Business Innovation Research (SBIR) Phase II project will develop new vision processing and inspection methods vital to enabling use of an automated, robotic strawberry harvester. Acceptance of automated harvesting technology by strawberry farmers hinges on the ability of the harvester to remove bad berries from the plant without allowing the undesirable berries from entering the harvester packaging stream and potentially contaminating large quantities of berries. To achieve this, it will be necessary for the harvester to identify and eliminate diseased, rotten, damaged, or infested berries at multiple stages in the stream from automated picking to final packaging. The classification method that identifies the berries to be eliminated will be extremely accurate, with a very high detection rate and a low false alarm rate. The methods developed will be suitable for installation on a farming machine that is subject to a harsh outdoor environment as well as the shock and vibration environment found on a robotic harvesting device. New handling processes will be developed that will allow automated inspection of the entire berry without damaging the fruit or creating a risk of cross contamination from infected berries, significantly advancing the state of the art for automated strawberry processing.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.