A visible light-thermal infrared multimodal image dataset collected from a real greenhouse tomato cultivation scene for flower and fruit detection

The rapid development of machine vision has promoted the automation of crop monitoring, providing technical support for planting management. For this purpose, this paper constructs a visible light-thermal infrared multimodal image dataset for the detection of flowers and fruits of facility tomatoes: it includes 300 images of the flowering period and 343 images of the fruiting period collected by the H982 visible light camera, as well as 145 images of the fruiting period collected by the HIKMIC thermal infrared camera. The LabelImg tool is used to complete the bounding box annotation of tomato flowers, fruits, and fruit maturity. Based on the evaluation of YOLOv13 (n/s/l), the mAP50 can reach over 90%, indicating the practicality of the dataset. The multimodal fusion experiment shows that compared with single visible light, the fusion strategy significantly improves the detection performance without sacrificing the inference efficiency almost at all, verifying the complementarity of thermal infrared and visible light, and providing data support for the research and deployment of intelligent monitoring algorithms for facility tomatoes.