Integrating reinforcement learning with YOLO for intelligent productivity analysis in object detection

In modern agriculture, smart farms leverage advanced technologies to boost productivity and optimize resource management, playing a critical role in ensuring global food security and economic sustainability. However, implementing smart farming technologies faces challenges in achieving precise monitoring and automated decision-making. While YOLO models are widely adopted for their real-time and accurate object detection, their application in agriculture requires complex parameter tuning and careful dataset selection, which can impact model performance and training efficiency. To address these challenges, this paper proposes an innovative framework that integrates reinforcement learning (RL) with YOLO to enhance intelligent productivity analysis in object detection. Our RL-based approach dynamically optimizes training parameters—such as learning rate, batch size, and data proportion—and selects high-quality pseudo-labeled data to streamline the YOLO training process. Through iterative learning, the RL agent identifies optimal strategies to improve model performance and training efficiency. Experimental results show that our method significantly enhances the mean Average Precision (mAP) of YOLO models while reducing training time, demonstrating the potential of combining RL with YOLO for more effective and efficient object detection systems. This work offers a novel perspective for optimizing deep learning model training in computer vision applications.