Research on Greenhouse Eggplant Fruit Detection and Tracking-Based Counting Using an Improved YOLOv5s-DeepSORT

Accurate fruit counting is essential for yield evaluation and automated management in greenhouse eggplant production. This study presents a lightweight detection and counting method based on an improved YOLOv5s–DeepSORT framework. To reduce computational cost while preserving accuracy, we replace the YOLOv5s backbone with MobileNetV3, insert an Efficient Channel Attention (ECA) module to enhance discriminative fruit features, and substitute the neck C3 block with C2f to strengthen multi-scale feature fusion. Compared with the original YOLOv5s, our improved YOLOv5s increases precision by 2.3% while reducing the number of parameters and FLOPs by 37.0% and 50.9%, respectively. For counting, we integrate DeepSORT with a counting-zone strategy that increments the count once per target when the bounding-box center first enters the counting zone, thereby mitigating identity switches (ID switches) and suppressing duplicate counts. Experimental results demonstrate that the proposed method enables accurate and real-time eggplant fruit counting in complex greenhouse scenes, providing practical support for automated yield assessment on inspection robots.