Multimodal image dataset of tomato fruits with different maturity

Accurately identifying tomatoes of different maturity and determining the appropriate harvest time are important prerequisites for achieving efficient tomato harvesting and ensuring post-harvest quality. However, in actual harvesting scenarios, complex lighting conditions can degrade the quality of RGB images, making it difficult for models to acquire and utilize key visual features, leading to application bottlenecks. Multimodal data has complementary and consistent properties, which can provide additional feature information for models. Depth and near-infrared images are mainstream multimodal research data due to their ease of acquisition and low cost. Additionally, tomato fruits have dense distribution and asynchronous maturity characteristics during the growth process. Existing tomato cluster detection and segmentation datasets are difficult to meet the needs of maturity-oriented harvesting. Building a multimodal image dataset of tomato fruits of different maturity based on visible light, depth, and near-infrared can effectively fill the gaps in the current research field. This dataset contains 4,000 sets of multimodal image data samples, covering tomato sample images under four lighting conditions: natural light, artificial light, weak light, and sodium yellow light. It includes target detection and semantic segmentation annotations for three maturity stages: unripe, half-ripe, and ripe. The total size is 10.4 GB. This dataset can provide basic data support for the development of visual intelligence systems for tomato intelligent management and harvesting equipment.