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

英国的软水果产业，50%的总生产成本专用于劳动力，是非常关注的采摘劳动力和劳动力成本通胀的可用性。解决这些挑战的一个有前途的解决方案是开发水果采摘机器人，实现水果生产的自动化。成功的机器人采摘系统的进一步发展的主要障碍是缺乏一般性的了解，水果品种是最适合的task.This博士项目提出开发新的自动化表型技术部署在一个移动的机器人提供高通量，高保真度的草莓品种指示其适合机器人收获。该研究将调查基于植物/果实几何形状（即3D）的技术，提供有关品种物候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局限性，利用自主的移动的机器人，使快速识别多个性状的领域。这一目标将通过以下目标来实现：验证现代机器人传感技术，用于直接在环境中对草莓植物/水果进行精确的3D传感;开发软件技术，使机器人能够从多个视图对环境进行精确的3D重建;开发技术，获得单个水果和植物的性状，最重要的是它们的空间安排，如分离和分组，这将决定它们是否适合机器人采摘;在移动的机器人上部署该系统，以实现快速和自动化操作;验证现有草莓品种是否可用于机器人采摘，以及该技术是否适用于新品种的种植者。该项目的交付成果：至少三种草莓品种的注释数据集;草莓环境重建的软件技术（即3D地图），测量单个植物成分的特征以及草莓植物和果实的空间排列;基于计算的特征与来自行业的人类专家的比较的评估和验证结果;通过真实的机器人采摘器的实验验证技术。本提案中概述的研究将有利于软水果行业，机器人公司，但也更广泛的学术界的基础知识和实际的解决方案，以表型驱动未来的浆果育种计划和新颖的机器人设计。或水果-采摘机器人是解决软果业面临的劳动力问题的一个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品种最适合这项任务的普遍了解。项目ScopeThis博士项目提出开发新的自动化表型技术部署在一个移动的机器人提供高通量，高保真指标的草莓品种，表明其适合机器人收获。该研究将调查基于植物/果实几何形状（即3D）的技术，提供有关品种物候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局限性，利用自主的移动的机器人，使快速识别多个性状的领域。机器人表型分析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型机器人设计。

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.