YOLO-plum: A high precision and real-time improved algorithm for plum recognition

Abstract

Real-time, rapid, accurate, and non-destructive batch testing of fruit growth state is crucial for improving economic benefits. However, for plums, environmental variability, multi-scale, occlusion, overlapping of leaves or fruits pose significant challenges to accurate and complete labeling using mainstream algorithms like YOLOv5. In this study, we established the first artificial dataset of plums and used deep learning to improve target detection. Our improved YOLOv5 algorithm achieved more accurate and rapid batch identification of immature plums, resulting in improved quality and economic benefits. The YOLOv5-plum algorithm showed 91.65% recognition accuracy for immature plums after our algorithmic improvements. Currently, the YOLOv5-plum algorithm has demonstrated significant advantages in detecting unripe plums and can potentially be applied to other unripe fruits in the future.

MSGV-YOLOv7: A Lightweight Pineapple Detection Method

“…Single-stage detection algorithms necessitate only one round of feature extraction for object detection. Examples of such algorithms include the Single Shot Multibox Detector (SSD) [16,17] and You Only Look Once (YOLO) [18][19][20][21] series, renowned for their swift detection speed and exceptional computational performance. Therefore, these algorithms are well suited for the real-time monitoring of agricultural targets in complex environments.…”