Height information acquisition method of seedling with machine vision

Author(s):  
Wenqiang Zhang ◽  
Wei Li ◽  
Zhenyu Yang ◽  
Jianda Han
Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 416
Author(s):  
Hui Wang ◽  
Jie Song

Aiming at the problem of insufficient integration and sharing of forestry information resources under the current communication network and the lack of the concept set of forestry information attributes, which leads to poor information retrieval performance, a fast retrieval method of forestry information features based on symmetry function is studied in depth, and the method is implemented by PDA (Personal Digital Assistant)-BA (Buliding Automation). Using the SED (Stream Editor) forestry information acquisition method under a communication network to collect forestry information, a forestry signal noise cancellation method based on symmetric function method is obtained. In order to improve the accuracy of forestry information acquisition, denoising of the signal in the information was carried out. Constructing forestry information data ontology, integrating forestry resources, establishing a conceptual set of forestry information attributes, distinguishing forestry information attributes, establishing a fast retrieval model of forestry information features based on the synonym library, and completing the fast retrieval of forestry information features. The experimental results show that the recall and precision of this method are 99.25% and 99.24%, respectively, and the retrieval performance is superior, which has a certain application value.


2020 ◽  
pp. 259-268
Author(s):  
Qinlan Li

The key to the design of the ground air dual-purpose agricultural information acquisition robot is the application of machine vision technology to realize the collection of crop growth state information. This research mainly designs the machine vision system of the ground air dual-purpose agricultural information acquisition robot, including hardware, software and image processing algorithm. The machine vision system designed in this paper can effectively complete the collection of crop status information. In order to verify the effectiveness of machine vision system, blueberry was used as the experimental object. The control group was set up indoor and outdoor, the fruit condition and quality information were detected, and the blueberry yield was estimated according to the test results. The experimental results show that the image segmentation algorithm in the vision system can identify blueberry fruit well, and the system has strong information analysis ability, and can accurately predict the quality and yield of blueberry fruit according to the image. It can be seen that the machine vision system has a good ability of information acquisition and recognition, which has a high reference significance for the design and research of the ground air dual-purpose agricultural information acquisition robot.


Magnetic Resonance Imaging (MRI) has been utilized broadly for clinical purposes to portray human anatomy due to its non-intrusive nature. The information acquisition method in MRI naturally picks up encoded signals (Fourier transformed) instead of pixel values and is called k-space information. Sparse reconstruction techniques can be executed in MRI for producing an image from fewer measurements. Compressive sensing (CS) technique samples the signals at a rate lower than traditional Nyquist’s rate and thereby reduces the data acquisition time in MRI. This paper investigates a new proposed sampling scheme along with radial sampling and 1D Cartesian variable density sampling. For various sampling percentages, subjective and quantitative analyses are carried out on the reconstructed Magnetic Resonance image. Experimental results depicts that the high sampling density near the center of k-space gives a better reconstruction of compressing sensing MRI.


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