V-YOLO: A Lightweight and Efficient Detection Model for Guava in Complex Orchard Environments

Abstract

The global agriculture industry is encountering challenges due to labor shortages and the demand for increased efficiency. Currently, fruit yield estimation in guava orchards primarily depends on manual counting. Machine vision is an essential technology for enabling automatic yield estimation in guava production. To address the detection of guava in complex natural environments, this paper proposes an improved lightweight and efficient detection model, V-YOLO (VanillaNet-YOLO). By utilizing the more lightweight and efficient VanillaNet as the backbone network and modifying the head part of the model, we enhance detection accuracy, reduce the number of model parameters, and improve detection speed. Experimental results demonstrate that V-YOLO and YOLOv10n achieve the same mean average precision (mAP) of 95.0%, but V-YOLO uses only 43.2% of the parameters required by YOLOv10n, performs calculations at 41.4% of the computational cost, and exhibits a detection speed that is 2.67 times that of YOLOv10n. These findings indicate that V-YOLO can be employed for rapid detection and counting of guava, providing an effective method for visually estimating fruit yield in guava orchards.