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

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 Scope
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. 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.