Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Online prediction method and system for agricultural greenhouse production

A prediction method and greenhouse technology, applied in the field of agricultural informatization, can solve problems such as long update cycle, achieve the effect of improving prediction accuracy, meeting real-time requirements, and reducing human resource costs

Pending Publication Date: 2021-04-16
SHANDONG UNIV +1
View PDF13 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In the traditional training method, the update cycle is relatively long after the model is put into use. Even if the prediction is wrong, it can only be corrected in the next update.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Online prediction method and system for agricultural greenhouse production
  • Online prediction method and system for agricultural greenhouse production
  • Online prediction method and system for agricultural greenhouse production

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0040] Such as figure 1 As shown, an online prediction method for agricultural greenhouse production, including:

[0041] Obtain historical data of agricultural greenhouse production and collect real-time data of agricultural greenhouse production;

[0042] Perform normalization processing on historical data and real-time data, and obtain training sample data after class encoding processing on historical data according to latitude and longitude information;

[0043] The data collected by the sensor is processed by downsampling, and the median of the data collected within one hour is passed into the model for training; on the one hand, downsampling can reduce the complexity of the model and effectively avoid the influence of abnormal points, on the other hand It can also reduce the number of valve adjustments to control environmental variables, thereby prolonging the service life of the valve;

[0044] Extract features from the training sample data and input them into the onl...

Embodiment 2

[0083] Taking tomato production as an example, the growth cycle of crops is divided into: germination period, seedling period, vegetative growth period, fruit growth period, and fruit maturity period. In order to predict the optimal growth conditions of a certain crop, it is necessary to obtain various environmental indicators in the greenhouse. The sensors installed in the greenhouse are used to obtain the current soil temperature and humidity, air temperature and humidity, light intensity, CO 2Concentration; the latitude and longitude of the location of the greenhouse can be obtained through GPS; the picture collection and identification device in the greenhouse is used to identify the type and growth cycle of the crop. After the obtained data are standardized, normalized or category encoded, these features are put into the online LightGBM model training to predict various environmental parameter indicators that meet the optimal growth conditions of crops. In order to avoid...

Embodiment 3

[0091] An online prediction system for agricultural greenhouse production, including:

[0092] Data acquisition module: obtain historical data of agricultural greenhouse production, and collect real-time data of agricultural greenhouse production;

[0093] Data processing module: normalize historical data and real-time data, and classify historical data according to latitude and longitude information to obtain training sample data;

[0094] Training module: extract features from the training sample data and input them into the online model for training to obtain the trained online model;

[0095] Prediction data acquisition module: input real-time data into the trained online model to obtain prediction data;

[0096] Parameter adjustment module: adjust various environmental parameter indicators of agricultural greenhouse production according to the forecast data.

[0097] Further, it also includes a video acquisition device, which sets different acquisition frequencies for d...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides an online prediction method and system for agricultural greenhouse production, and the method comprises the steps: obtaining the historical data of agricultural greenhouse production, and collecting the real-time data of agricultural greenhouse production; normalizing historical data and real-time data, and performing category coding on the historical data according to the longitude and latitude information to obtain training sample data; extracting features from the training sample data, and inputting the features into an online model for training to obtain a trained online model; inputting the real-time data into a trained online model to obtain prediction data; adjusting various environmental parameter indexes of agricultural greenhouse production according to the prediction data; giving a prediction result in a targeted manner; considering the influence of longitude and latitude on crop growth and processing the longitude and latitude by a Geohash coding technology, so that the generalization ability of features is improved; the defect of poor prediction implementation is overcome, the prediction precision of the model can be improved, and the real-time requirement of the system can be met.

Description

technical field [0001] The disclosure belongs to the field of agricultural informatization, and specifically designs an online prediction method and system for agricultural greenhouse production. Background technique [0002] In the current production of economic crops, agricultural greenhouses can make full use of solar light and heat resources, which can reduce environmental pollution and reduce the occurrence of diseases and insect pests while saving resources, so they are widely used. With the development of technology, the structure and materials of the current agricultural greenhouses have been relatively perfect. The problem to be solved urgently is how to predict the best growth conditions for each growth stage of different crops, and then increase the yield. Although it is quite effective to use the traditional algorithm to predict, there is a problem that the real-time performance is not strong. Therefore, it is very necessary to design an online prediction system ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/00G06K9/62G06Q10/04G06Q50/02G01D21/02
CPCY02A40/25
Inventor 陈桂友王晓彤席斌
Owner SHANDONG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com