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

Automatic detection method for cotton crack open stage

An automatic detection and bell period technology, which is applied in the intersection of digital image processing and agricultural meteorological observation, can solve the problems of large errors, long cotton growth cycle, time-consuming and labor-consuming, etc., and achieve high accuracy, unique characteristics, and information volume rich effect

Active Publication Date: 2015-07-01
武汉昂格睿景科技有限公司
View PDF4 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] For a long time, the information related to the cotton development period has been recorded mainly by manual observation and recording. The observation results will be affected by the subjective factors of the observers, resulting in relatively large errors; at the same time, due to the long growth cycle of cotton, cotton planting The range is wide, and the method of only using manual observation is time-consuming and labor-intensive
At present, there is no method to automatically observe and predict the cotton boll opening period

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
  • Automatic detection method for cotton crack open stage
  • Automatic detection method for cotton crack open stage
  • Automatic detection method for cotton crack open stage

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0110] Build a cotton boll classifier:

[0111] First, a picture with a size of 90×90 pixels is selected as positive and negative samples for training. Among them, the positive samples are pictures of cotton bolls in different postures. The edges of the bolls in the boll pictures are clear and full, and the boll cracking can also be observed by the human eye; the negative samples are sub-image pictures of non-cotton bolls in the longitudinal front view of the cotton field.

[0112] Then, the SIFT feature quantity in the obtained positive and negative sample pictures is extracted, and the SIFT feature quantity is subjected to local constrained linear (LLC) encoding to obtain the positive training sample and negative training sample feature quantity input to the classifier.

[0113] Finally, use the support vector machine (SVM) as the classifier, select the radial basis (RBF) kernel function, input the feature quantities of positive training samples and negative training samples...

Embodiment 2

[0115] Using the method provided by the invention to judge figure 2 Whether the cotton in the box has entered the boll splitting period:

[0116] (1) Obtain the position of cotton bolls in the picture.

[0117] (1-1) Collect longitudinal front view image sequences of cotton fields.

[0118] The camera is 0.3 meters above the ground, the focal length is 14 mm, the horizontal shooting direction is north, the angle with the horizon is 0 degrees, and the resolution is 4 million pixels. figure 2 shown.

[0119] (1-2) Coarse search for the position of cotton bolls.

[0120] Firstly, the image obtained in step (1-1) is split to obtain sub-images of 90×90 pixels according to the order from top to bottom and from left to right, with a step size of 30 pixels. The boll classifier obtained in Example 1 is used to judge the sub-images to obtain the tag value of each sub-image. Set the coarse search threshold to be the boll classifier threshold when the false positive rate is between...

Embodiment 3

[0150] Using the method provided by the invention to judge Figure 8 Whether the cotton in the box has entered the boll splitting period:

[0151] (1) Obtain the position of cotton bolls in the picture:

[0152] (1-1) Collect images of cotton fields

[0153] The camera is 0.3 meters above the ground, the focal length is 14 mm, the horizontal shooting direction is north, the angle with the horizon is 0 degrees, and the resolution is 4 million pixels. Figure 8 shown.

[0154] (1-2) Coarse search for the position of cotton bolls.

[0155] Firstly, the image obtained in step (1-1) is split to obtain sub-images of 90×90 pixels according to the order from top to bottom and from left to right, with a step size of 30 pixels. The boll classifier obtained in Example 1 is used to judge the sub-images to obtain the tag value of each sub-image. Set the coarse search threshold to be the boll classifier threshold when the false positive rate is equal to 9%. Record the sub-image positi...

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 an automatic detection method for cotton crack open stage based on images. The method comprises the steps of (1) acquiring all cotton boll positions in a cotton land picture sequence; (2) performing cotton boll image segmenting at all recorded cotton poll positions in the last cotton land image; (3) determining whether the cotton land enters the crack open stage according to the segmented cotton boll image. According to the method, the important parameters which represent the cotton growth condition are used as the determining basis to determine the cotton crack open state, so that the detection result is high in accuracy; the method is of important significance on determining the stating time of the cotton crack open period, analyzing the relationship between the cotton developing stage and the weather condition, identifying the agricultural weather condition for cotton growth and guiding farmers to timely perform agricultural activity.

Description

technical field [0001] The invention belongs to the intersecting field of digital image processing and agricultural meteorological observation, and in particular relates to an automatic detection method for cotton boll-opening stage. Background technique [0002] Cotton is one of the main economic crops in China, and China's cotton output is also in the leading position in the world. The boll splitting period of cotton is an important developmental period in the growth of cotton. This developmental period is the key period for the formation of cotton yield and quality, so it is also an important period for cotton field management. During this period, the activity of cotton roots gradually weakens, and the ability to absorb nutrients decreases significantly. The focus of production is to preserve roots and leaves, prevent premature aging, increase boll weight, and prevent pests and diseases. Therefore, the monitoring and identification of cotton boll splitting stage is very ...

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): G06K9/66G06T7/00
Inventor 曹治国李亚楠
Owner 武汉昂格睿景科技有限公司
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