High-throughput robotic phenotyping of fruit traits for automatic strawberry harvesting

用于自动草莓采收的水果性状的高通量机器人表型

• 批准号: 2618899
• 金额: --
• 依托单位: University of Lincoln
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2618899
• 关键词: throughput; robotic; phenotyping; fruit; traits

The UK's soft fruit industry, with 50% of the total production cost dedicated for labour, is extremely concerned with both the availability of picking labour and labour cost inflation. One of the promising solutions addressing these challenges is the development of fruit-picking robots enabling automation of the fruit production. The main obstacle for further development of successful robotic picking systems is a lack of general understanding which fruit variety is the most suitable for the task.This PhD project proposes to develop new automated phenotyping techniques deployed infield on a mobile robot providing high-throughput, high-fidelity indicators of strawberry varieties indicating their suitability for robotic harvest. The study will investigate techniques based on plant/fruit geometry (i.e. 3D) providing traits about the phenology of the variety and external fruit and plant characteristics. The approach will overcome the limitations of the laboratory-based phenotyping systems by exploiting an autonomous mobile robot to enable rapid identification of multiple traits in the field. This aim will be addressed through the following objectives:Validation of the modern robotic sensing technology for precise 3D sensing of the strawberry plants/fruit directly in the environment;Development of software techniques enabling the robot a precise 3D reconstruction of the environment from multiple views;Development of techniques which derive traits of individual fruit and plants and most importantly their spatial arrangements such as separation and groupings which will determine their suitability for the robotic picking;Deployment of the system on a mobile robot for rapid and automated operation;Validation of the existing strawberry varieties for their use in robotic picking and suitability of the techniques for the growers of new varieties.The deliverables of the project:Annotated data sets of at least three strawberry varieties;Software techniques for reconstructing the strawberry environment (i.e. 3D map), measuring traits of individual plant components and spatial arrangement of strawberry plants and fruit;Evaluation and validation results based on comparisons of calculated traits against the human experts from the industry;Validation of techniques through the experiments with a real robotic picker.The research outlined in this proposal will benefit the soft fruit industry, robotics companies but also the wider academic community with the fundamental knowledge and practical solutions to phenotyping driving future berry breeding programs and novel robot designs.Or Fruit-picking robots are a promising technological solution to the labour problems faced by the soft fruit industry. The main obstacle for further development of successful robotic picking systems is a lack of general understanding which fruit variety is the most suitable for the task. Project ScopeThis PhD project proposes to develop new automated phenotyping techniques deployed infield on a mobile robot providing high-throughput, high-fidelity indicators of strawberry varieties indicating their suitability for robotic harvest. The study will investigate techniques based on plant/fruit geometry (i.e. 3D) providing traits about the phenology of the variety and external fruit and plant characteristics. The approach will overcome the limitations of the laboratory-based phenotyping systems by exploiting an autonomous mobile robot to enable rapid identification of multiple traits in the field. The fundamental knowledge and practical solutions to robotic phenotyping will benefit the soft fruit industry, robotics companies and academia, driving future berry breeding programs and novel robot designs.

英国的软果产业，总生产成本的50%专门用于劳动力，对采摘劳动力的可用性和劳动力成本通胀都非常担忧。解决这些挑战的一个有希望的解决方案是开发能够实现水果生产自动化的水果采摘机器人。进一步开发成功的机器人采摘系统的主要障碍是缺乏对哪个水果品种最适合这项任务的普遍了解。这个博士项目建议开发新的自动化表型技术，部署在移动机器人上，提供高通量、高保真的草莓品种指示其适合机器人收获的指标。这项研究将研究基于植物/水果几何学(即3D)的技术，提供有关品种物候特征和外部果实和植物特征的特征。该方法将克服基于实验室的表型系统的限制，利用自主移动机器人来实现对田间多种性状的快速识别。将通过下列目标实现这一目标：验证直接对环境中的草莓植物/水果进行精确三维传感的现代机器人传感技术；开发使机器人能够从多个视角精确三维重建环境的软件技术；开发得出单个水果和植物的特征以及最重要的是它们的空间安排的技术，例如将确定它们是否适合机器人采摘的分离和分组；在移动机器人上部署该系统，以实现快速和自动化操作；验证现有草莓品种在机器人采摘中的使用和新品种种植者技术的适宜性。该项目的交付成果：至少三个草莓品种的注释数据集；重建草莓环境的软件技术(即3D地图)，测量草莓植株和果实的单个植物组成部分的特征和空间排列；基于计算的特征与该行业人类专家的比较而得出的评估和验证结果；通过与真实的机器人采摘者的实验来验证技术。这项提案中概述的研究将使软果行业、机器人公司以及更广泛的学术界受益，为表型识别驱动未来的浆果育种计划和新的机器人设计提供基本知识和实用解决方案。或者水果采摘机器人是解决软果行业面临的劳动力问题的一种有前途的技术解决方案。进一步开发成功的机器人采摘系统的主要障碍是缺乏对哪种水果最适合这项任务的普遍理解。项目范围这个博士项目建议开发新的自动化表型技术，部署在移动机器人上，提供高通量、高保真的草莓品种指示，表明它们适合机器人收获。这项研究将研究基于植物/水果几何学(即3D)的技术，提供有关品种物候特征和外部果实和植物特征的特征。该方法将克服基于实验室的表型系统的限制，利用自主移动机器人来实现对田间多种性状的快速识别。机器人表型的基础知识和实用解决方案将使软果行业、机器人公司和学术界受益，推动未来的浆果育种计划和新颖的机器人设计。