Application of novel machine learning techniques and high speed 3D vision algorithms for real time detection of fruit

October 2017 – September 2021

Justin LeLou√ędec

Novel digital technologies including vision systems, robotics, and autonomous systems are seen as potential game-changers for the horticulture sector. Visions systems can be used to assess and sense the crop to enable better decision support; robotics and autonomous systems offer new means to drive productivity. These issues apply to all soft and top fruits, but also more widely across the whole fresh produce sector. However, all picking and vision systems are dependent on the development of complex algorithms developed to identify, measure and locate fruit in real-time. The development of these systems is not trivial, especially in outdoor environments where the background light level and quality can change within an instant.

The main objective of this project is to deploy novel machine learning technologies to detect, locate and measure (size and colour) fruit in real-time. This work fundamentally underpins the development of all crop-picking robots. This project will initially focus on strawberry and be anticipated to include apple.

Research progress