Method for carrying out early warning on fruit tree

A fruit tree and temperature prediction technology, which is applied in the field of freezing early warning for fruit trees based on the curve classification modeling method, can solve the problems of large workload and poor operability, and achieves the goal of reducing early warning costs, workload and economic losses. Effect

Active Publication Date: 2013-09-25
ZHEJIANG UNIV
View PDF3 Cites 1 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The invention provides a method for early warning of freezing of fruit trees, which solves the shortcomings of traditional methods such as heavy workload and poor operability in solving the problem of predictive modeling of large-scale time series curves

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
  • Method for carrying out early warning on fruit tree
  • Method for carrying out early warning on fruit tree
  • Method for carrying out early warning on fruit tree

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0026] Embodiment 1 The establishment of freezing early warning model

[0027] During the growth period of the cherry tree (in this example, from April 13th to June 5th, a total of 54 days), 48 sensors collect data every 6 to 12 seconds, and in this embodiment, data is collected every 10 seconds , and store the collected data in the corresponding data storage.

[0028] The central processing unit retrieves the data in the data memory, analyzes and processes these data and models them. Since the present embodiment is an early warning to the freezing point, only the time from sunset to sunrise of the next day (20:09:30PM) is selected. -05:45:30AM) to analyze the data collected in the segment.

[0029] First, for each sensor, draw the original temperature curve (54 curves in total) for the data collected from sunset to sunrise of the next day (20:09:30PM-05:45:30AM) in 54 days, and for each temperature Calculate the growth rate of the chain from the curve, and accumulate the de...

Embodiment 2

[0036] Embodiment 2 Freezing warning

[0037] In the next growth period of the cherry tree, input the current time point (that is, which day) and the location to predict the temperature (that is, which sensor), select the corresponding temperature model, and use the temperature model to calculate the predicted temperature of the corresponding location. If the temperature is lower than 0°C, an alarm will be issued. The essence of early warning of temperature is to find the corresponding temperature model at that moment.

[0038] Taking the temperature value of the No.48 sensor on a certain day as an example for analysis, use the real-time temperature information collected on that day, such as the temperature information corresponding to the time (20:09:30PM-21:00:00PM), and calculate the relative value of these temperature values The growth rate of the chain is calculated, and the cumulative development speed of the chain is accumulated to obtain the cumulative growth curve of t...

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 discloses a method for carrying out early warning on a fruit tree. The method comprises the steps that (1) one fruit tree is selected randomly, a plurality of temperature sensors are installed on the fruit tree, and temperature data of the fruit tree in the whole growing season are collected; (2) for the collected temperature data of each sensor in the whole growing season, firstly dimension conversion is eliminated for data of every single day to obtain link relative development speed accumulation curves, then curve classification is carried out on all the curves, and modeling is carried out on the curves of the same kind to obtain temperature forecast models corresponding to various time points; (3) in a later growing season of the fruit tree, the current time point and a position with the temperature to be forecast are inputted, the corresponding temperature models are chosen, and the forecast temperatures of the corresponding positions are obtained through calculation of the temperature models. The method resolves the problems that according to a traditional method, when forecast modeling of the large-scale time sequence curves is carried out, the workload is high, and operability is poor.

Description

technical field [0001] The invention relates to the research field of early warning of freezing of fruit trees, in particular to a method for early warning of freezing of fruit trees based on a curve classification modeling method. Background technique [0002] The damage of freezing to fruit trees may directly affect the yield and quality of fruits. Although different fruit trees have different tolerances to freezing, it is very important for orchard managers to take appropriate freezing preventive measures to reduce economic losses caused by freezing damage. Common antifreeze protection methods in several orchards include heating fans, sprinkler irrigation, etc. For example, the Chinese invention patent application with the publication number CN101543229A discloses a kind of antifreeze aerosol for fruit trees, which consists of 5 to 8 parts of dry sawdust, 2 to 4 parts of ammonium nitrate , 1 to 2 parts of waste diesel oil, 1 to 3 parts of coal powder, and 1 to 2 parts of ...

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): G01K13/00G08B21/18
Inventor 邵咏妮陈纳何勇
Owner ZHEJIANG UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products