Multimodal Dataset for Agricultural Machinery Weeding Operations

The agricultural sector is facing challenges in crop growth monitoring and weed management, where traditional manual inspections are inefficient and prone to errors. Existing automated solutions lack adaptability to varying environments and precision, making it difficult to efficiently handle different operational conditions. This dataset aims to provide high-quality image data to enhance the object detection capabilities of agricultural machinery in weeding operations. The dataset includes multimodal images from different farmland environments, captured using professional equipment and collected under various lighting and weather conditions to ensure data diversity and representativeness. The data undergoes multiple rounds of annotation and expert review to ensure accuracy and consistency, finally stored in JPEG format and structured with JSON files for related information.