A dataset of strawberry multimodal image target detection

<p indent="0mm">Accurate detection of strawberries during their growth period has always been a critical technical challenge for precision orchard management. To address the lack of publicly available multimodal image datasets in the field of strawberry object detection, this dataset was constructed through field data collection and standardized data processing, and includes multimodal image data of strawberries covering different growth throughout the fruit development period and a wide range of natural environmental conditions. The dataset contains visible light, depth, and infrared images, with a total data volume of 31.36 GB. To ensure the high quality and reliability of the strawberry multimodal image target detection dataset, a rigorous data quality control method was used, including standardized acquisition protocols, abnormal frame filtering, annotation consistency checks, multimodal alignment and calibration, as well as periodic verification of data integrity and readability. It provides valuable foundational data resources for multimodal image data fusion and target detection research and can serve as a benchmark dataset for training and testing deep learning models, demonstrating significant practical value in advancing strawberry target detection research.