Rice yield prediction method

A forecasting method and production forecasting technology, applied in forecasting, instrumentation, data processing applications, etc., can solve the problems of rare production estimates and the failure to achieve the expected results, and achieve the effect of accurate estimation

Inactive Publication Date: 2017-01-04
SHENYANG AGRI UNIV
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Problems solved by technology

[0003] In the actual application of yield estimation, the estimation of rice yield is mostly realized by using high-altitude remote sensing as the data source. However, due to the great influence of the underlying surface, atmospheric effects, and

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  • Rice yield prediction method
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Examples

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[0044] Example 1

[0045] Provide a kind of test area as the northeast region below, the test material is the prediction method of the output of sunken rice 47 varieties, specifically implement according to the following steps:

[0046] Step 1, select a test plot, and select two sampling points in the test plot, and the two sampling points are evenly distributed in the test plot, and each sampling point is a square area of ​​1m×1m.

[0047] Step 2, within the number of days of rice growth, obtain the rice leaf normalized vegetation index in the test plot at 12:00 every day, and obtain the sum of all rice leaf normalized vegetation indexes to obtain the sum of NDVI;

[0048] Wherein, the rice leaf normalized vegetation index is obtained according to the following steps:

[0049] Taking all the leaves in the sampling point as sampling objects to obtain the single-leaf normalized vegetation index of each leaf;

[0050] Calculate the average value of the single-leaf normalized v...

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Abstract

The invention discloses a rice yield prediction method. According to the rice yield prediction method, a test district is selected; rice leaf normalization vegetation indexes of the test district are acquired, a sum value of all the rice leaf normalization vegetation indexes is solved to acquire an NDVI sum value; rice leaf photochemical vegetation indexes of the test district are acquired, a sum value of all the rice leaf photochemical vegetation indexes is solved to acquire a PRI sum value; rice yield of the test district is acquired; a multiple regression analysis method of SPSS software is utilized, linear relationships among the rich yield, the NDVI sum value and the PRI sum value are analyzed, and a rich yield prediction model is acquired; the rich yield is predicted according to the rice yield prediction model. Through the method, the relationships between japonica rice leaf NDVI and PRI values at different growth periods and japonica rice yield change can be timely monitored, japonica rice yield estimation business operation can be realized, high efficiency, rapid and precise rice yield estimation is realized, and precise prediction of the japonica rice yield can be realized, and a japonica rice growth state can be effectively tracked.

Description

technical field [0001] The invention belongs to the technical field of crop growth information monitoring, and in particular relates to a method for predicting rice yield. Background technique [0002] In the field of rice research, the vegetation index calculated according to the spectral characteristics of plant-sensitive bands can quickly, non-destructively and quantitatively characterize the growth status of rice, monitor rice growth and predict rice yield. Since the Normalized difference vegetation index (NDVI) was first proposed by scientists in 1973, it has attracted extensive attention from experts and scholars due to its stability, and has become one of the most used vegetation indices in the past two decades. It is widely used to study crop growth and yield estimation; scientific research has found that changes in the reflectance of leaves at 531nm and 570nm can well reflect the light use efficiency (LUE) of leaves. Based on the reflectance of these two bands, the...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/02
CPCG06Q10/04G06Q50/02
Inventor 陈春玲马航许童羽于丰华郭雷吕东
Owner SHENYANG AGRI UNIV
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