ASPEN study case: Real time in situ apples detection and characterization

Abstract

Due to an increasing demand for food and pressures on our ecosystem, mechanisation and automation in agriculture has been proposed as one of the main solutions to the problems associated with overpopulation given today's life standards. To encourage the use of new technologies and bridge the gap between plant and computer science, here we validate an open-source pipeline capable of predicting real time in situ fruit characteristics, specifically in this case for apples. Using Agroscope's phenotyping tool (ASPEN), we achieve an average precision at intercept over union of 50 % of 0.75 when using YOLOv8 - m as the object detection algorithm, and with thanks to the use of multiple sensors, we find an average diameter error of 4.4 mm in the task of apple size determination. Our research demonstrates that although the pipeline tends to underestimate the actual fruit size, size estimation cannot only be used to determine the size of apples per scan, but also to track temporal apple size distribution in 4 different varieties. This research supports ASPEN in potentially replacing traditional field measurements, also suggesting that other traits could also be digitally measured for standard orchard phenotyping, either for scientific or agricultural output goals. Finally, we make publicly available a new dataset of more than 600 images (Agroscope_apple dataset) and a pre-trained model based on YOLOv8 and specifically trained for the in-situ apple detection task. By doing so, we hope to increase the accessibility and use of new technologies in the field of agriculture.

Keywords

• Machine learning
• YOLOv8
• Apple
• SLAM
• Terrestrial laser scanner