Lightweight Detection Method for Real-Time Monitoring Tomato Growth Based on Improved YOLOv5s

Abstract

In order to monitor the growth and development of tomatoes, and improve the efficiency of flower and fruit thinning and tomato picking, this paper constructs a tomato flower and fruit dataset and proposes a TF-YOLOv5s model for the detection of tomato flowers and fruits in natural environments. Based on the YOLOv5s model, a C3Faster module is introduced to reduce the number of parameters and calculations while maintaining detection accuracy. The regular convolution is replaced by depth-wise separable convolution (DWConv) to avoid parameter redundancy. To improve the convergence and accuracy of the model, this paper replaces Complete Intersection over Union (CIoU) loss with Efficient Intersection over Union (EIoU) loss. The Squeeze-and-Excitation (SE) module is added to improve the model’s sensitivity to the features of the tomato flowers and fruits. Compared with the baseline model, the number of parameters is reduced by 54.5%, the weight file is reduced by 52.8%, the Floating-point Operation Per second (FLOPs) is reduced by 48.7%. The detection accuracy of tomato flowers and fruits [email protected] has improved by 1.4% and 1.2% respectively. TF-YOLOv5s is used to detect three types of targets: tomato flowers, red tomatoes, and green tomatoes, and [email protected] of which can reach as high as 95.2%. Furthermore, the improved algorithm is deployed on two edge computing devices to verify its effectiveness. Experimental results show that the algorithm in this paper can achieve high detection with less computational resources. This algorithm has the potential value of application in practical tomato production.

Keywords

• Tomato flower fruit recognition
• C3Faster
• convolutional neural networks
• lightweight