Deep Convolutional Neural Network enabled unmanned agricultural machine visual navigation system: architecture design, model optimization and empirical evaluation

Abstract

In response to the pressing need for intelligent navigation technology in modern agriculture, this paper introduces a visual navigation system for unmanned agricultural machinery based on deep convolutional neural networks (CNNs). The system integrates the strong representational power of deep learning with rich visual information to facilitate high-precision, adaptive autonomous navigation in complex agricultural environments. Key innovations encompass: (1) the development of a customized deep CNN model proficient in extracting critical features from agricultural images, such as obstacles, landmarks, and crop rows; (2) the integration of the CNN model with visual SLAM (Simultaneous Localization and Mapping) technology for real-time localization and three-dimensional mapping of agricultural landscapes; (3) the creation of a decision-making system that merges deep learning predictions with conventional path planning algorithms to ensure machinery navigates around obstacles while adhering to optimal trajectories. Experimental validation across diverse agricultural scenarios demonstrates that the proposed visual navigation system sustains high localization accuracy (RMSE < 0.2 meters) and robust obstacle avoidance performance (success rate exceeding 95%) even in GPS-denied or weak GPS environments. Furthermore, the system significantly improves the continuity and efficiency of machinery operations by reducing unnecessary repositioning and redundant tasks. Practically, it is readily integrable into existing agricultural machinery platforms, offering broad applicability and potential for widespread adoption.