In the development of modern agriculture, the intelligent use of mechanical equipment is one of the main signs for agricultural modernization. Navigation technology is the key technology for agricultural machinery to control autonomously in the operating environment, and it is a hotspot in the field of intelligent research on agricultural machinery. Facing the accuracy requirements of autonomous navigation for intelligent agricultural robots, this paper proposes a visual navigation algorithm for agricultural robots based on deep learning image understanding. The method first uses a cascaded deep convolutional network and hybrid dilated convolution fusion method to process images collected by a vision system. Then, it extracts the route of processed images based on the improved Hough transform algorithm. At the same time, the posture of agricultural robots is adjusted to realize autonomous navigation. Finally, our proposed method is verified by using non-interference experimental scenes and noisy experimental scenes. Experimental results show that the method can perform autonomous navigation in complex and noisy environments and has good practicability and applicability.

在现代农业的发展中，机械设备的智能使用是农业现代化的主要标志之一。导航技术是农业机械在运行环境中自主控制的关键技术，是农业机械智能化研究的热点。面向智能农业机器人自主导航的精度要求，提出一种基于深度学习图像理解的农业机器人视觉导航算法。该方法首先使用级联深度卷积网络和混合膨胀卷积融合方法来处理视觉系统收集的图像。然后，基于改进的霍夫变换算法提取处理图像的路径。同时，调整农业机器人的姿势，以实现自主导航。最后，通过无干扰的实验场景和嘈杂的实验场景对我们提出的方法进行了验证。实验结果表明，该方法可以在复杂嘈杂的环境中进行自主导航，具有良好的实用性和适用性。