Strawberry detection and counting based on YOLOv7 pruning and information based tracking algorithm

TL;DR

This paper enhances strawberry detection and tracking by pruning YOLOv7 detection heads for speed and accuracy, and introducing an information-based tracking algorithm that outperforms traditional methods in precision and reliability.

Contribution

It proposes an optimized pruning of YOLOv7 detection heads and an innovative IBTA for improved strawberry flower and fruit detection and tracking.

Findings

Pruning YOLOv7-tiny achieves 163.9 fps and 89.1% accuracy.

IBTA outperforms centroid tracking in MOTA and MOTP metrics.

IBTA shows better IDF1, IDR, IDP, MT, and ID metrics than CTA.

Abstract

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.