A dataset of multimodal images of tomato fruits at different stages of maturity

<p indent="0mm">Accurately identifying tomato ripeness and determining the optimal harvest time are crucial prerequisites for efficient tomato picking and ensuring post-harvest quality. However, in practical harvesting scenarios, complex lighting conditions can degrade the quality of RGB images, limiting the feature extraction capabilities of models and affecting application performance. Moreover, existing datasets for tomato cluster detection and segmentation are insufficient for meeting the requirements of maturity-targeted harvesting during the asynchronous ripening process of tomatoes. Constructing a multimodal image dataset of tomatoes at various stages of ripeness, using visible light, depth, and near-infrared imaging, can effectively fill this research gap. This dataset was collected using both a mechanized platform and manual-assisted methods to capture video stream data. Through frame extraction, redundancy reduction, modality alignment, and manual selection, 4,000 multimodal image samples were obtained. The dataset include tomato samples under four lighting conditions: natural light, artificial light, low light, and sodium yellow light. Additionally, it also has annotations for object detection and semantic segmentation at three ripeness stages: unripe, semi-ripe, and ripe, with a total data volume of 10.08 GB. This dataset performs well with YOLOX and DeepLabv3+ models and provides fundamental data support for the development of vision-based intelligent systems in tomato’s smart management and harvesting equipment.