A Multimodal Image-Text Dataset for Potted Vegetables

However, its key operational steps still heavily rely on manual labor, creating an urgent need for intelligent technologies to achieve precise management. Currently, most methods for recognizing the growth status of facility vegetables are based on single-modal RGB images, which are susceptible to lighting variations, plant occlusion, and substrate background interference in complex pot cultivation environments, making it difficult to be put into operation in real production scenarios.

Moreover, existing agricultural datasets are mostly focused on open-field or orchard settings, lacking multimodal image-text data resources, specifically for potted vegetables. This limitation hinders the development and application of multimodal fusion technologies and vertical-domain large models in this field. To address the issues, this study constructs a multimodal image-text dataset of potted vegetables covering multiple crop types and growth stages, 185.36 GB in total.