Multimodal Fusion for Optimizing Weed Control and Harvesting in Farmland

In the process of promoting agricultural production efficiency and sustainability, precise weed management is particularly crucial for mechanized weeding and the distribution of crop growth, which directly impacts the quality and yield of crops. This study introduces and implements a multi-modal fusion weed segmentation system based on the Transformer architecture, aimed at enhancing the accuracy of weed and crop recognition in farmlands. The system employs a dynamic fusion strategy, integrating data from various sensors flexibly, which significantly improves its recognition capabilities in complex environments. It also develops a feature enhancement algorithm for depth images, utilizing pixel intensity values to effectively enhance the expressive power of image information, thereby further increasing the accuracy of weed recognition. This system effectively combines information from RGB and depth images, achieving a leading precision level with an Intersection over Union (IoU) score of 79.54% on the public dataset WE3DS. These achievements not only advance the development of intelligent agricultural robotics but also provide valuable references for further research in related fields.