LMCNet: A MobileNetV4-Enhanced YOLOv10 with Cross-Scale Fusion for Tomato Ripeness Detection

In order to quickly and accurately identify tomato fruit ripeness and im-plement automated tomato harvesting in agricultural environments, this study proposes a lightweight tomato ripeness detection model based on an im-proved YOLOv10. Firstly, a lightweight model based on the improved YOLOv10 is proposed by introducing the Universal Inverted Bottleneck module from the MobileNetV4 network and integrating it with the C2f mod-ule in YOLOv10, Then, a new feature fusion structure is designed, where the C2fUIB module replaces the original feature fusion module in the CCFM structure, and the GhostConv module is introduced to replace the standard Conv module. The improved model efficiently handles and fuses the different scale information, and at the same time enhances the model’s detection accuracy and computational efficiency for tomato fruits. The results of this research model on tomato fruit ripeness detection show that the accuracy, recall and average precision are 88.2 , 86.2 and 90.2 , respectively, and the number of parameters of the network model is 4.62M, and the model memory occupancy is 9.7MB, which has a high detection precision and low number of parameters. It highlights the effect of the improved model on tomato fruit ripeness detection.