基于机器视觉的玉米果穗产量组分性状测量方法

玉米果穗的穗长、穗粗、穗行数、行粒数等性状是制约玉米产量的重要组分性状,目前主要采用人工测量方式,或通过截取果穗横断面图像自动计算穗行数等参数,操作复杂、测量效率低、主观误差大,且无法保留完整的原始考种材料。针对上述问题,该文基于机器视觉技术,通过可见光二维成像获取果穗三维表型性状参数,结合果穗颜色特征及果穗的生物学规律,分别建立投影修正模型、穗行数快速估算模型、行粒数计算模型等,精确计算穗长、穗粗、穗行数以及行粒数等性状参数。试验结果表明,该方法适用于粘连果穗处理,秃尖的识别率高,且对光照环境要求低,穗行数及行粒数的零误差率在93%以上,测量速度可达30穗/min以上,能够满足高通量考种的需求,特别是保留了原始果穗考种材料实现无损测量,对于实现高通量考种及精细化育种有重要的参考价值。

Measurement method for yield component traits of maize based on machine vision

Abstract: The maize variety test is an important link in the process of crop genetic breeding. The different maize varieties will produce a large number of varieties phenotype data, which need to be collected, collated, recorded, statistically analyzed and stored. Some phenotype data are related to the maize yield, such as bald rate, ear rows, row grains and so on. These maize characters are often collected by the traditional manual measurement at present. For example, the ear rows can be calculated by the maize section image which destroys the maize to be tested .Another measurement method for the ear rows is to rotate and scan the maize, which is very difficult to meet the needs of high throughput maize variety test. Aiming at the above problems, the calculation model according to the color and biological features of maize has been constructed based on the machine vision technology in this paper. The calculation model can compute the maize character parameters precisely, such as bald rate, ear rows, row grains and so on. The experimental results show that the calculation measurement has the high recognition precision and speed. The ear length ,ear diameter ,ear rows ,row grains and other yield components are taken as example for verifying the above calculation model in this paper. The experimental environment settings for image acquisition model are as follows: non wide-angle CMOS pinhole camera (portable, low cast), acquisition environment of soft light and bright place (no special light source set). The camera is 5 million pixels, and the image resolution is 2942 pixels ? 1944 pixels. Shoot height is 55 cm, the shooting format is to A3. The algorithm is tested by the PC machine which is configured as a dual core cpu (1.9 GHz) and 2 GB ram. The method presented in this paper can overcome these disadvantages of traditional manual measurement, such as low efficiency, subjective error, and unable to retain the integrity of the original maize material. The method presented in this paper can fetch the parameters of 3D phenotypic traits based on the 2D visible light imaging, and separately establish the projection correction model, the rapid estimation model and the calculation model for ear rows and row grains .The method presented in this paper can calculate the ear length, ear diameter (the calculation accuracy can reach more than 97 percent), ear rows and row grains of maize accurately. The zero error rates of ear rows measured by the method presented in this paper can reach 93 percent, and the absolute error of row grains measured by the method presented in this paper is about 2 grains. In this paper, the acquisition speed for the maize characters also has been tested, and the experimental results show that the measurement speed is up to 30 per minute above. With the promotion of the PC machine parameters, the measuring speed can be greatly improved. The methods proposed in this paper have important reference value to achieve the high throughput test and fine breeding.