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Crop harvesting amount prediction method and device

A forecasting method and crop technology, applied in the field of deep learning, can solve the problems of incomplete data and low accuracy, and achieve the effect of comprehensive data, huge stock, and improved forecasting accuracy

Pending Publication Date: 2021-05-11
SHANGHAI ZHONGXIN INFORMATION DEV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, in the above several forecasting methods, the data for forecasting are not comprehensive and the accuracy is not high

Method used

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  • Crop harvesting amount prediction method and device
  • Crop harvesting amount prediction method and device
  • Crop harvesting amount prediction method and device

Examples

Experimental program
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Embodiment 1

[0030] This embodiment provides a method for predicting the yield of crops, see figure 1 Shown is a flow chart of a crop harvest forecasting method, the crop harvest forecast method includes the following steps:

[0031] Step S102, acquiring monitoring data of crops based on global positioning system, geographic information system and remote sensing technology.

[0032] 3S technology is based on Global Positioning System (GPS), Geographic Information System (GIS) and Remote Sensing Technology (RS). A new comprehensive technology formed by organically forming a whole. It integrates information acquisition, information processing, and information application, and is outstanding in the high-speed, real-time and high-precision and highly quantifiable aspects of information acquisition and processing. 3S technology has been widely used in agricultural fields such as land use survey and soil erosion monitoring.

[0033] In this embodiment, the 3S technology is used to monitor the...

Embodiment 2

[0045] This embodiment provides another method for predicting the yield of crops, which is implemented on the basis of the above-mentioned embodiments; this embodiment focuses on describing the specific implementation of training the subjective evaluation model. see figure 2 The flow chart of another crop harvest forecasting method shown, the crop harvest forecast method in this embodiment includes the following steps:

[0046] Step S202, acquiring monitoring data of crops based on global positioning system, geographic information system and remote sensing technology.

[0047] see image 3 A schematic diagram of a crop harvest forecasting method is shown, and the monitoring data includes crop growth monitoring data, climate environment monitoring data, and pest and weed monitoring data. The monitoring data of crops can be obtained through the following steps: determine the planting area of ​​the crops at the specified coordinates based on the global positioning system; map ...

Embodiment 3

[0077] Corresponding to the above-mentioned method embodiment, the embodiment of the present invention provides a harvest forecasting device for crops, see Figure 5 A structural schematic diagram of a harvest forecasting device for crops shown, the harvest forecasting device for crops includes:

[0078] The monitoring data acquisition module 51 is used to obtain the monitoring data of crops based on the global positioning system, geographic information system and remote sensing technology;

[0079] The feature data determination module 52 is used to determine the feature data corresponding to the monitoring data; the feature data characterizes the data that affects the harvest of crops;

[0080] The harvest forecasting module 53 is used to input the feature data into the pre-trained regression tree model, and output the harvest forecast data of the crops; wherein, the regression tree model is constructed based on the scalable regression tree algorithm.

[0081] A harvest for...

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Abstract

The invention provides a crop harvesting amount prediction method and device. The method comprises the following steps: acquiring monitoring data of crops based on a global positioning system, a geographic information system and a remote sensing technology; determining feature data corresponding to the monitoring data, wherein the feature data represents data influencing the harvesting amount of the crops; inputting the feature data into a pre-trained regression tree model, and outputting crop harvest amount prediction data, wherein the regression tree model is constructed based on a scalable regression tree algorithm. In the mode, growth data of crops in different stages of the whole life cycle can be monitored through a global positioning system, a geographic information system and a remote sensing technology, data used during modeling is comprehensive and huge in stock, and prediction accuracy can be improved to the maximum extent from the data angle; the regression tree model is constructed based on a scalable regression tree algorithm, the algorithm can synchronously carry out increase and decrease changes of the data volume and the calculated amount, and the model has scalability.

Description

technical field [0001] The present invention relates to the technical field of deep learning, in particular to a method and device for predicting harvest volume of crops. Background technique [0002] With population growth and food safety issues becoming increasingly serious, crop yields are under pressure to improve. In addition, global climate change has caused inestimable losses to crops in different regions, so it is necessary to monitor the farm environment and crop growth in real time. [0003] For the harvest of crops, it can be predicted by the information of several growth factors in the greenhouse, simple statistics and predictions can be made based on the historical harvest data of crops, or forecast by remote sensing technology. However, in the above several forecasting methods, the data for forecasting are not comprehensive and the accuracy is not high. Contents of the invention [0004] In view of this, the object of the present invention is to provide a m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/02G06F16/2458G06F16/29G06K9/62G01S19/42G01N21/17
CPCG06Q10/04G06Q50/02G06F16/2465G06F16/29G01S19/42G01N21/17G01N2021/1793G06F18/24323
Inventor 张曙华杨安荣邬旭栋马睿涛
Owner SHANGHAI ZHONGXIN INFORMATION DEV
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