Truss tomato detection under artificial lighting in greenhouse using BiSR_YOLOv5

Abstract

The visual characteristics of greenhouse-grown tomatoes undergo significant alterations under artificial lighting, presenting substantial challenges in accurately detecting targets. To address the diverse appearances of targets, we propose an improved You Only Look Once Version 5 (YOLOv5) model named BiSR_YOLOv5, incorporating the single-point and regional feature fusion module (SRFM) and the bidirectional spatial pyramid pooling fast (Bi-SPPF) module. In addition, the model adopts SCYLLA-intersection over union loss instead of complete intersection over union loss. Experimental results reveal that the BiSR_YOLOv5 model achieves F1 and mAP@0.5 scores of 0.867 and 0.894, respectively, for detecting truss tomatoes. These scores are 2.36 and 1.82 percentage points higher than those achieved by the baseline YOLOv5 algorithm. Notably, the model maintains a size of 13.8M and achieves real-time performance at 35.1 frames per second. Analysis of detection results for both large and small objects indicates that the Bi-SPPF module, which emphasizes finer feature details, is better suited for detecting small-sized targets. Conversely, the SRFM module, with a larger receptive field, is better suited for detecting larger targets. In summary, the BiSR YOLOv5 test results validate the positive impact of accurate identification on subsequent agricultural operations, such as yield estimation or harvest. This is achieved through the implementation of a simple maturity algorithm that utilizes the process of "finding flaws."