A precise berry counting method for in-cluster grapes to guide berry thinning

In table grape production, berry thinning is a vital management practice where workers remove berries to achieve a target number per cluster. However, this process fundamentally depends on obtaining an accurate initial berry count, which currently relies on manual methods. These conventional approaches are labor-intensive, slow, and error-prone, posing a significant bottleneck to efficient and precise vineyard management. This study proposes a method comprising a dual-branch network named MVDNet and a post-processing algorithm. MVDNet simultaneously performs density map regression for berry counting and bunch segmentation. Its architecture employs a Front-end containing UIB modules for feature extraction, multi-scale feature fusion for spatial detail reconstruction, and a parameter-free SimAM attention mechanism to enhance salient berry features. Extensive experiments demonstrate that our method achieves competitive performance, with MVDNet attaining a Mean Absolute Error (MAE) of 7.7, a Root Mean Square Error (RMSE) of 12.6, and a Mean Intersection Over Union (MIoU) of 0.90 on the test set. Remarkably, our model delivers this high accuracy with extremely low computational resource consumption, containing only 3.372 million parameters, underscoring its suitability for deployment on resource-constrained edge devices. Furthermore, the subsequent post-processing algorithm for per-cluster berry counting achieves a high coefficient of determination (R²) of 0.886. The proposed solution thus provides a robust, efficient, and practical tool for automated berry counting, facilitating precise vineyard management and contributing to enhanced grape quality and productivity.