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Agricultural machine operation area calculation method and system based on machine learning

A technology of machine learning and computing methods, applied in the direction of neural learning methods, computing, computer components, etc., can solve problems such as weak generalization ability, large measurement error of large fields, unsuitable for multi-scenario applications, etc., to ensure relative accuracy , the calculation result is the best effect

Active Publication Date: 2022-06-24
传为佳话(武汉)资讯科技有限公司
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The above-mentioned trajectory-based calculation method has weak generalization ability and is not suitable for multi-scenario applications. For example, the method of calculating the actual operating area based on the displacement length of agricultural machinery multiplied by the operating width has obvious errors when it comes to heavy tillage operations. ; Another example is the operation metering algorithm based on the operation track of the agricultural machinery space to set the operation buffer zone. Its operation efficiency is low when the area of ​​the operation plot is too large; another example is the improved Alpha-Shape algorithm, which is more suitable for small areas and large For the calculation of regular fields, the error rates for the measurement of small farmland and irregular farmland area are: 3.5%, more than 5%, but the measurement error for large fields is relatively large

Method used

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  • Agricultural machine operation area calculation method and system based on machine learning
  • Agricultural machine operation area calculation method and system based on machine learning
  • Agricultural machine operation area calculation method and system based on machine learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0130] Step 1: Data acquisition: use python language to write a script, call the network interface, and obtain the number of trajectories of the tractor numbered E 0100326 on 20211112. The trajectory data is as follows Figure 10 shown;

[0131] Step 2: Data processing: First, the parking point is removed, and the processed data is as follows: Figure 11 shown, and then perform lof outlier detection and elimination, the effect is as follows Figure 12 Then calculate its Hopkins statistic as: 0.9519594251698345, judge that its Hopkins volume is greater than 0.8, then perform dbscan clustering on it, after dbscan clustering, the result of plot division is the following three pieces of land, such as Figure 13 shown;

[0132] Lot 1: Operation time 2021-11-12 07:39:33 2021-11-12 08:51:21

[0133] Plot 2: Working time 2021-11-12 08:51:27 2021-11-12 10:20:12

[0134] Lot 3: Working time 2021-11-12 10:20:48 2021-11-12 11:11:54

[0135] Here, an arbitrary job plot is selected for...

Embodiment 2

[0145] Step 1: Data acquisition: use python language to write a script, call the network interface, and obtain the trajectory data of the rice transplanter numbered E 0100899 on 2021-09-27. Figure 16 shown;

[0146] Step 2: Data processing: First, the parking point is removed, and the processed data is as follows: Figure 17 Shown: Then perform lof outlier detection and elimination, the effect is as follows Figure 18As shown, then calculate its Hopkins statistic as: 0.9829652086685471, and judge that its Hopkins volume is greater than 0.8, then perform dbscan clustering on it. After dbscan clustering, the result of plot division is the following piece of land, such as Figure 19 shown;

[0147] Lot 1: Operation time 2021-09-27 07:49:09 2021-09-27 10:33:33

[0148] Here, select job plot 1 and perform the following steps

[0149] Filter the trajectory data of the machine from 07:49:09-10:33:33 on that day, such as Figure 20 shown;

[0150] Step 3: Trajectory classificat...

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Abstract

The invention discloses an agricultural machinery operation area calculation method and system based on machine learning. The system comprises a terminal for realizing an acre calculation algorithm and a storage medium. The invention aims to solve the problem of low working area calculation precision of a single mu calculation algorithm under various driving tracks and various complex working scenes. The method comprises the following specific steps: acquiring operation data of an agricultural implement, and removing a track drift point and a stay point by using a lof algorithm; performing clustering segmentation on the data in time and space by using a Hopkins statistic and a dbsca algorithm so as to obtain an operation plot; using a CNN algorithm to identify a track type; calculating the tillage missing rate according to the image pixel points; and selecting a proper interpolation method and an area calculation method through a decision tree algorithm, and calculating the operation area. Compared with an existing algorithm, the method is suitable for various operation scenes and various operation track types, the influence of missing tillage on the area measurement result is avoided, and the accuracy of operation area calculation is effectively improved.

Description

technical field [0001] The invention belongs to the field of agricultural machinery operation area calculation methods, and more particularly relates to a machine learning-based agricultural machinery operation area calculation method and system. Background technique [0002] When the smart agricultural system helps farmers to farm, manage and collect unmanned operations, it is necessary to accurately monitor the operation area of ​​each link, so as to carry out scientific management and control of the whole process. At the same time, in the process of implementing the national agricultural machinery operation subsidy, there is a phenomenon of subsidy, and the actual area of ​​grain cultivation still needs to be further accurately verified. How to accurately measure the operating area is the need for smart agriculture to scientifically control agricultural production and verify the state's operating subsidies. [0003] At present, the agricultural machinery operating area m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06V10/762G06V10/72G06V10/764G06V10/82G06N3/04G06N3/08G06T7/62
CPCG06N3/08G06T7/62G06T2207/10004G06N3/045G06F18/2321G06F18/241G06F18/24323G06F18/10Y02A40/10
Inventor 齐浩周婷杨帆熊振
Owner 传为佳话(武汉)资讯科技有限公司
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