  The strawberry industry yields significant economic benefits for Florida, yet
the process of monitoring strawberry growth and yield is labor-intensive and
costly. The development of machine learning-based detection and tracking
methodologies has been used for helping automated monitoring and prediction of
strawberry yield, still, enhancement has been limited as previous studies only
applied the deep learning method for flower and fruit detection, which did not
consider the unique characteristics of image datasets collected by the machine
vision system. This study proposed an optimal pruning of detection heads of the
deep learning model (YOLOv7 and its variants) that could achieve fast and
precise strawberry flower, immature fruit, and mature fruit detection.
Thereafter, an enhanced object tracking algorithm, which is called the
Information Based Tracking Algorithm (IBTA) utilized the best detection result,
removed the Kalman Filter, and integrated moving direction, velocity, and
spatial information to improve the precision in strawberry flower and fruit
tracking. The proposed pruning of detection heads across YOLOv7 variants,
notably Pruning-YOLOv7-tiny with detection head 3 and Pruning-YOLOv7-tiny with
heads 2 and 3 achieved the best inference speed (163.9 frames per second) and
detection accuracy (89.1%), respectively. On the other hand, the effect of IBTA
was proved by comparing it with the centroid tracking algorithm (CTA), the
Multiple Object Tracking Accuracy (MOTA) and Multiple Object Tracking Precision
(MOTP) of IBTA were 12.3% and 6.0% higher than that of CTA, accordingly. In
addition, other object-tracking evaluation metrics, including IDF1, IDR, IDP,
MT, and IDs, show that IBTA performed better than CTA in strawberry flower and
fruit tracking.
