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Navigation System Research and Design Based on Intelligent Image Classification Algorithm of Extreme Learning Machine

机译:基于极限学习机智能图像分类算法的导航系统研究与设计

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摘要

The Navigation System discussed in the paper can recognize ripe fruit automatically for agricultural harvesting machinery by machine vision technology, and then achieve the purpose of autonomous positioning and navigation through autonomous path planning. In order to achieve this process, agricultural machinery positioning and navigation system must have high precision and fast image processing algorithm. Based on this, this paper introduces the extreme learning machine algorithm into the agricultural machinery navigation system, combined with BP neural network algorithm, through the determination of the image coordinates of ripe fruit and fruit tree, to achieve the rapid navigation of agricultural machinery operation. In order to verify the feasibility of the scheme, the computational efficiency and precision of the algorithm are counted. The experimental results show that the picking efficiency has been improved obviously and the picking accuracy has been improved by using the extreme learning machine, which can meet the design requirements of modern agricultural machinery and equipment.
机译:本文所讨论的导航系统可以通过机器视觉技术自动识别农业收割机的成熟果实,然后通过自主路径规划实现自主定位和导航的目的。为了实现这一过程,农机定位和导航系统必须具有高精度和快速的图像处理算法。在此基础上,将极限学习机算法引入农机导航系统,结合BP神经网络算法,通过确定成熟果树和果树的图像坐标,实现农机运行的快速导航。为了验证该方案的可行性,对算法的计算效率和精度进行了计算。实验结果表明,采用极限学习机可以明显提高采摘效率,提高采摘精度,可以满足现代农业机械设备的设计要求。

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