Crop grain character measuring method

Abstract

The invention discloses a crop grain character measuring method. According to the method, the length, the width, the grain number and the thousand grain weight of crop grains can be rapidly measured and calculated in batches. The method comprises the following steps that 1, assembling a computer, a usb camera, a light-emitting table and a non-transparent box body to construct a crop grain measuring platform; 2, weighing a sample, and spreading and paving the sample on a light-emitting table; 3, writing a program, and sequentially completing data file establishment, blank image acquisition, length calibration, grain image acquisition, sample name and weight entry, image processing and segmentation, connected domain judgment and error correction, measurement data output and the like; automatically completing image analysis and data output after the name and weight of the sample are input by a program, whereinonly a few seconds are needed; measurement results are directly output in a format recognized by office and statistical software and used for further arrangement and statistics. The method has the advantages of being low in cost, easy to operate, rapid, accurate, high in practicability and extremely easy to maintain.

Description

technical field [0001] The invention belongs to the fields of crop genetics and breeding and crop cultivation, and more specifically relates to a method for measuring crop grain traits. Background technique [0002] Grain traits are an important factor affecting crop grain yield and quality, and an indispensable evaluation index for yield and quality in agricultural production and scientific research. Taking rice as an example, grain type has a significant impact on rice grain weight. Grain length-width ratio is an important indicator in rice quality appraisal standards. Grain number per panicle and thousand-grain weight (100-grain weight) are both components of rice yield. Yield has a decisive influence. Therefore, the investigation of crop grain traits has always been an important part of the test work. [0003] At present, the routine testing steps of crops, such as rice, corn, soybean, etc., are still mainly performed manually. Among them, the measurement methods of g...

AI Technical Summary

Problems solved by technology

[0005] At the same time, there are also electronic counting devices, such as electronic counting instruments, the counting error can fluctuate in the range of 0.4%-1.5% with the change of grain shape, but the counting speed is even lower than the manual speed; in recent years, Huazhong University of Science and Technology has developed The automatic seed testing instrument "CN101933436B" was adopted, and the measurement speed of the grain traits is fast, but its counting error is higher than that of the electronic counting instrument, and the cost is extremely expensive, which is difficult for ordinary production and scientific research units to afford.

[0006] In addition, some researchers have proposed a grain counting method based on image processing, but the imaging system is still sensitive to ambient light, which affects the measurement accuracy and is not conducive to field operations; the image analysis algorithm as the core application has defects, which can easily lead to deformation of the grain image However, it cannot measure length and width traits, and cannot continuously identify pictures, so it is difficult to quickly apply to batch determination

[0007] To sum up, the current method of grain traits inspection has the following problems: 1. It is labor-intensive and time-consuming, and it is very difficult to face a large number of samples

Taking rice as an example, the manual counting is generally 3-5 minutes per 1000 grains, the measurement of grain length and grain width generally takes 5 minutes per sample, and the measurement and recording of all traits takes 15-20 minutes per sample. Sample appears inefficient

2. Manual counting errors are relatively large, and repeated boring counting operations for a long time may easily lead to different degrees of errors; existing counting algorithms based on image processing have defects and cannot measure other traits

3. Existing testing instruments are expensive, complex in structure and difficult to maintain

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