Unlock instant, AI-driven research and patent intelligence for your innovation.

Fresh corn maturity prediction method based on unmanned aerial vehicle remote sensing and deep learning

A fresh corn, deep learning technology, applied in neural learning methods, computer parts, character and pattern recognition, etc., can solve problems such as the inability to accurately reflect the maturity of fresh corn, and the harvest period of uneaten corn. , to achieve the effect of good maturity, high precision and saving time and cost

Pending Publication Date: 2022-03-04
HUAZHONG AGRI UNIV
View PDF0 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

At present, there are few studies on judging the harvest time of fresh corn, especially in the aspect of extracting the characteristics of fresh corn ears and tassels, almost all of which are based on tassels, and most of them are mainly based on tassels to estimate yield
And the tassel of fresh corn is on the upper part of the corn, but its change is not as sensitive as the ear, so it cannot accurately reflect the maturity of fresh corn
In previous studies, no specific solution was proposed for the harvest period of fresh corn

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
  • Fresh corn maturity prediction method based on unmanned aerial vehicle remote sensing and deep learning
  • Fresh corn maturity prediction method based on unmanned aerial vehicle remote sensing and deep learning
  • Fresh corn maturity prediction method based on unmanned aerial vehicle remote sensing and deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] In order to solve the technical problem adopted by the present invention, a method for predicting maturity of fresh corn based on remote sensing of UAV and deep learning is provided. The method flow chart of the method is as follows: figure 1 shown.

[0032] A method for predicting the maturity of fresh corn based on UAV remote sensing and deep learning, characterized in that it includes the following steps:

[0033] Step S1, using a UAV platform equipped with a high-definition digital camera to collect high-throughput, mid-to-late earing data collection on fresh corn crop plants to obtain continuously changing dynamic phenotypic data;

[0034] Step S2, manually collecting several strain samples in the field to obtain the measured values ​​of water content and sugar content of fresh corn;

[0035] Step S3, for the image obtained in step S1, use the deep learning neural network to accurately identify the ear area of ​​fresh corn;

[0036] Step S4, for the ear area iden...

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 fresh corn maturity prediction method based on unmanned aerial vehicle remote sensing and deep learning. According to the method, corn ears are identified by using a deep learning technology, and a model for estimating the sugar content and the water content of the fresh corn ears is constructed by combining a vegetation index extracted by unmanned aerial vehicle remote sensing data and a ground artificial sample measured value, and is used for predicting the maturity of the whole field plant fresh corn. The random forest model established by the method has relatively high robustness and can adapt to various field conditions. The requirement for the sensor is not high, and the cost of purchasing the sensor by the user is greatly reduced.

Description

technical field [0001] The invention belongs to the field of agricultural automation, and in particular relates to a method for predicting the maturity of fresh corn, in particular to a method for predicting the maturity of fresh corn based on UAV remote sensing and deep learning. Background technique [0002] The maturity of fresh corn will affect the taste and nutritional value of fresh corn, which is related to the market value of fresh corn. The water content and sugar content of fresh corn are important reference standards to reflect its maturity. Therefore, constructing a prediction model for the maturity of fresh corn can provide decision-making for the picking period of fresh corn. [0003] The application of UAV remote sensing technology can realize low-cost, high-efficiency, and non-destructive prediction of sugar content and moisture content of fresh corn. At present, there are few studies on judging the harvest time of fresh corn, especially in the aspect of ext...

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): G06V20/10G06V10/26G06N3/04G06N3/08
CPCG06N3/08G06N3/045
Inventor 张建王宏铭王楚锋谢静
Owner HUAZHONG AGRI UNIV