A Review of Integrated Approaches in Robotic Raspberry Harvesting

Raspberry cultivation represents a high-value global industry; however, concerns regarding its sustainability have been raised due to the high costs and labour shortages associated with manual harvesting. These challenges represent significant motivators for the development of robotic systems. This review article analyses contemporary robotic harvesting technologies, with a particular focus on integrated systems, machine vision and end-effectors. A review of the relevant literature was conducted in order to identify and compare the main development trends represented by academic and commercial prototypes. The analysis demonstrates that deep learning methodologies, most notably YOLO architectures, predominate within the domain of machine vision, thereby ensuring the effective identification and assessment of fruit ripeness. In order to ensure that the handling of the subject is done in a gentle manner, it is recommended that soft robotic end-effectors which are equipped with sensors and which minimise mechanical damage be used. In view of the fact that the number of studies focusing directly on raspberries is limited, the present study also analyses transferable technologies from other types of soft fruit. Consequently, future research should concentrate on integrating machine vision models that have been trained using raspberries and developing advanced soft end-effectors with integrated tactile sensors.