Fallen apple detection as an auxiliary task: Boosting robotic apple detection performance through multi-task learning

Abstract

In modern agricultural practices, advanced machine learning techniques play a pivotal role in optimizing yields and management. A significant challenge in orchard management is detecting apples on trees, which is essential for effective harvest planning and yield estimation. The YOLO series, especially the YOLOv8 model, stands out as a state-of-the-art solution for object detection, but its potential in orchards remains untapped. Addressing this, our study evaluates YOLOv8’s capability in orchard apple detection, aiming to set a benchmark. By employing image augmentation techniques like exposure, rotation, mosaic, and cutout, we lifted the model's performance to a state-of-the-art level. We further integrated multi-task learning, enhancing tree apple detection by also identifying apples on the ground. This approach resulted in a model with robust accuracy across evaluation metrics. Our results underscore that the YOLOv8 model achieves a leading standard in orchard apple detection. When trained for both tree and fallen apple detection, it outperformed the one when trained exclusively for the former. Recognizing fallen apples not only reduces waste but could also indicate pest activity, influencing strategic orchard decisions and potentially boosting economic returns. Merging cutting-edge tech with agricultural needs, our research showcases the promise of multi-task learning in fruit detection with deep learning.

Keywords

• Automatic agriculture
• Deep learning
• Detection of orchard apple
• YOLOv8 model
• Data augmentation techniques