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This paper introduces an automatic decision-making localization algorithm for agricultural robots by fusing Generative Big Models (GBMs) with Agriculture Internet of Things (AIoT). The approach uses a Realsense D435i camera on the robot to acquire real-time environmental data, which is then processed by the GBM using a novel reinforcement learning method based on human solid feedback (S-RLHF) and a weakly supervised fine-tuning data generation method (WS-FT DAGM). Experimental results demonstrate a significant improvement in operational efficiency, reaching over 92% compared to traditional CNN-based methods.
Robots get a 92% efficiency boost in agriculture by fusing generative AI with IoT, leaving traditional CNN methods in the dust.
Robot perception difficulties in complex environments seriously affect robot operational efficiency. The application of generative big model (GBM) technology on robots through the Agriculture Internet of Things (AIoT) can solve the difficulty of low-operational efficiency. Therefore, this article proposes an automatic decision-making localization algorithm for robots fused with GBMs in AIoT. First, in the AIoT, the complex agricultural scene information is acquired in real-time by the Realsense D435i equipped on the robot, which is accessed in the GBM through a proprietary network to realize real-time intelligent sensing and control between the environmental information and the robot. Then, the innovative reinforcement learning method based on human solid feedback (S-RLHF) and the automatic generation method of weakly supervised fine-tuning data (WS-FT DAGM) are designed in the generative large model. At the same time, by combining the characteristics of the robot operation, two generative significant model recommendation methods are designed, which solves the problem of the difficulty of the target perception in the complex agricultural scene. Finally, by integrating the AIoT and generative large models, the critical method of real-time analysis of crop shading characteristics by the large model is innovatively proposed to solve the problem of low efficiency in robot operation. In the robot test experiments, the operation efficiency using the fusion of AIoT and generative large model reaches more than 92%, significantly improving the operation efficiency compared to the small model (CNN) method without AIoT and generative large model in the traditional agricultural robot.
