A lightweight multi-view detection and counting method for real-time cherry tomato yield estimation using greenhouse inspection robots

Crop yield prediction is one of the key measures for dynamically adjusting harvest schedules and minimizing production losses. To address the production requirements for multi-object real-time detection and accurate counting of tomatoes in complex greenhouse environments, this study proposes a lightweight “detection–tracking–counting–compensation” cascaded network framework, optimized for deployment on inspection robots. First, a sparse convolution module (G-FConv) and a lightweight multi-scale feature aggregation module (L-MFA) were designed, and a lightweight feature extraction network was constructed to reduce computational load and complexity. Second, a lightweight asymmetric decoupled head incorporating the SimAM attention mechanism was developed to enhance critical feature extraction while further reducing parameter count. Third, a multi-view cooperative detection and compensation method was proposed to optimize multi-object tracking performance in dense and occluded scenes. Finally, TensorRT-based inference acceleration was applied to improve the robot’s real-time processing capability. The proposed model achieves high detection (90.8%) and counting (95.2%) accuracy with a compact size of 3.98 MB and 5.3 GFLOPs, while the proposed MVC method reduces counting errors by 95.6%. This study provides an effective technical solution for precise tomato yield prediction in greenhouses, offering significant practical value for optimizing harvest schedules and reducing production losses.