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Method and system for detecting imperfect grains of granular crops

A detection method, crop technology, applied in the direction of neural learning methods, biological neural network models, instruments, etc., can solve the accuracy, repeatability and generalization of imperfect classification results, shorten the sorting time of inspectors, imperfect In order to achieve the effect of improving the scope of application, high accuracy of the model, and efficient sorting

Active Publication Date: 2020-10-09
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

[0003] Aiming at the above-mentioned deficiencies in the prior art, the present invention proposes a method and system for detecting imperfect grains of granular crops, using artificial intelligence technology based on deep learning to construct a classification model, and improving the accuracy of the imperfect classification results of granular crops. Repeatability and generalization, it can be used for the detection of imperfect kernels of various granular crops, which greatly shortens the sorting time of the inspector, avoids the shortcomings of different screening standards from person to person, and also avoids the problem caused by the work of the inspector. Fatigue caused an increase in the error rate

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  • Method and system for detecting imperfect grains of granular crops
  • Method and system for detecting imperfect grains of granular crops

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Embodiment Construction

[0026] In this embodiment, wheat will be used as the crop to be tested for specific description. Such as figure 1 As shown, this embodiment relates to a system for detecting imperfect grains of granular crops, including: crop image acquisition device 1, image information processing device 2 and crop auxiliary sorting device 3, wherein: crop image acquisition device 1 collects and outputs The image information of the crops to be tested is sent to the computer 4 as the image information processing device 2, and the image information processing device 2 performs analysis and processing according to the image information and outputs the processing results to the display screen 5 as the crop auxiliary sorting device 3, and the crop auxiliary sorting device 3 Carry out imperfect grain sorting prompt array image display.

[0027] Such as figure 2As shown, the crop image acquisition device 1 includes: a stage 6, an upper camera 7, a lower camera 8, an upper light source 9, a lower ...

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Abstract

The invention discloses a method and system for detecting imperfect grains of granular crops. Crops are arranged in order; upper and lower side image information of crops is collected, the upper and lower side image information is preprocessed, and image classification is performed through a classification model to obtain a corresponding crop imperfect grain classification report; a correspondingimperfect grain sorting prompt array image is generated according to an image classification result, and crop imperfect grain sorting collection is performed according to the imperfect grain sorting prompt array image. According to the invention, an artificial intelligence technology based on deep learning is utilized; construction of classification model is performed, the accuracy, repeatabilityand generalization of imperfect classification results of granular crops are improved, the method and system can be used for detecting imperfect grains of various granular crops, greatly shortening the sorting time of detectors, avoiding the disadvantage that the screening standard varies from person to person, and also solving the problem that the error rate is increased due to the work fatigue of the detectors.

Description

technical field [0001] The invention relates to a technology in the field of image processing applications, in particular to a method and system for detecting imperfect grains of granular crops. Background technique [0002] At present, the detection of imperfect grains of granular crops mainly relies on artificial visual sensory detection, which takes a long time, and with the increase of detection working time, the error rate may increase due to fatigue. In addition, there are subjective differences in the detection standards of each inspector. , leading to inconsistencies in the detection results of the same batch of samples; some research institutions use traditional image processing methods to extract and analyze artificially designed features from the collected images of imperfect grain crops to obtain imperfect grain classification results. However, the image features of imperfect granules are complex and the area is small. In addition, the difference in visual featur...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08G06Q50/02
CPCG06N3/084G06Q50/02G06V20/38G06V10/44G06N3/045G06F18/24G06F18/253
Inventor 柴新禹陈坚品李恒
Owner SHANGHAI JIAO TONG UNIV
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