Intelligent agricultural machine operation management method and system

Abstract

The embodiment of the invention provides an intelligent agricultural machine operation management method and system, and the method comprises the steps of: obtaining a plurality of land parcel local images collected by an unmanned aerial vehicle, and the pose information of the unmanned aerial vehicle corresponding to the land parcel local images; splicing all the land parcel local images according to the corresponding unmanned aerial vehicle pose information to obtain a land parcel image; inputting the land parcel image and the key points of an operation area marked by a user into a trained semantic segmentation network model to obtain the operation area; and determining a path planning result according to the operation area, and sending the path planning result to the intelligent agricultural machine. According to the method, the land parcel image is obtained by splicing a plurality of land parcel local images collected by the unmanned aerial vehicle, manual collection by means of a high-precision GPS is not needed, collection by means of an agricultural machine provided with a high-precision GPS is also not needed, the farmland land parcel information obtaining mode is simplified, the data collection efficiency is high, and the precision requirement can be met. User experience and an artificial intelligence algorithm can be effectively combined, and the segmentation precision of the neural network is improved.

Description

technical field [0001] The invention relates to the technical field of intelligent agricultural machinery, in particular to an intelligent agricultural machinery operation management method and system. Background technique [0002] In order to realize the autonomous operation of agricultural machinery, it is necessary to obtain high-precision farmland boundary information and plan a reference path covering all operating areas. [0003] At this stage, in order to obtain high-precision farmland plot information, it is necessary to use high-precision GPS manual collection or use agricultural machinery equipped with high-precision GPS to collect by recording the edge of the plot and recording the driving track. The data collection efficiency based on agricultural machinery is low, the sampling density is low, and the soil compaction caused by agricultural machinery is serious. The accuracy of farmland detection results based on satellite remote sensing data cannot meet the requ...

AI Technical Summary

Problems solved by technology

The data collection efficiency based on agricultural machinery is low, the sampling density is low, and the soil compaction by agricultural machinery is serious

The accuracy of farmland detection results based on satellite remote sensing data cannot meet the requirements for the accuracy of land plot information for the autonomous operation of agricultural machinery

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