文献类型：期刊文献

中文题名：玉米定向精播种粒形态与品质动态检测方法

英文题名：Dynamic Detection of Corn Seeds for Directional Precision Seeding

作者：刘长青[1];陈兵旗[2];张新会[2];王侨[2];杨曦[2]

第一作者：刘长青

通讯作者：Chen, Bingqi

机构：[1]北京联合大学机电学院;[2]中国农业大学工学院

第一机构：北京联合大学机器人学院

年份：2015

卷号：46

期号：9

起止页码：47-54

中文期刊名：农业机械学报

外文期刊名：Transactions of the Chinese Society for Agricultural Machinery

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；

基金：国家高技术研究发展计划(863计划)资助项目(2012AA10A501-5)

语种：中文

中文关键词：玉米;定向播种;种粒;精选;图像检测

外文关键词：Corn Directional seeding Seed Selection Image detection

摘要：为满足玉米定向精播对种子外形和品质的要求,设计了一种玉米种子精选装置,并研究了玉米种粒动态检测算法。经过脱粒并筛除杂质的种粒投入玉米种子精选装置,分两列两层传输,完成玉米种粒的动态检测。通过计算种子胚根尖端的方向,排除了种粒的重复检测现象;以人工选取的100粒标准种粒外形参数为基础建立合格种粒特征参数库,实现对种粒外形的检测;依据合格种粒和重度霉变种粒表皮亮度差异较大的特点,基于图像饱和度分量对重度霉变种粒加以检测;依据轻度霉变种粒表皮呈现块斑的特点,利用种粒的R、G、B颜色平均值检测轻度黑色霉变;以种粒黄色区域补洞后对应原种粒(B-R)的值,判断种粒的轻度白色霉变和轻度破损;对于外形和霉变检测合格的种粒,通过分析种粒区域中白色区域的大小,进行玉米种粒胚芽朝向的判断,为后续种粒定向包装和定向播种提供了依据。对280粒各品种玉米种子进行实时检测,每粒种子的平均检测时间约为14 ms,重复种粒判断准确率为95%,种粒合格性检测准确率为96.1%,胚芽朝向判断准确率为97.1%。
High-quality seeds can increase the germination rate. Directional seeding can make corn blades grow regularly and enhance ventilation and light energy utilization in the field. These two are necessary conditions to achieve directional and precision seeding for corn seeds. This paper provided a device and an image detection algorithm of corn seeds for directional and precision seeding. Those unqualified corn seeds were found from the corn seed samples and the corn embryo direction of the rest qualified seeds were determined using this detection algorithm. The corn seeds were transferred in two lines by conveyors. Two cameras at different locations captured the transferred corn seeds at the rate of 50 frames per second. The same seed in continuous images needed to be detected only once. So the repeated corn seed images were judged and not detected. The seed region and outer contour were detected. The shape characteristic parameters, such as the area of the seed region and the perimeter of the outer contour, were calculated. According to the color of the embryo of the corn seed as close to white and the endosperm was close to yellow, the furthest point of the white part from the yellow area center was determined as the tip point of the corn seed. The axis through the tip point and the centroid point was defined as the major axis. The axis through the centroid and perpendicular to the major axis was defined as the minor axis. The angle ct between the major axis and the horizontal direction was calculated. And on this basis, the shape characteristic parameters such as the length of major axis, the length of minoraxis, tile length-width ratio, the degree of symmetry and the duty ration, were calculated quickly. The 100 qualified corn seed samples were randomly selected as standard seeds. The above shape characteristic parameters were detected successively. A qualified range was determined according to the standard seeds detection result. The unqualified corn seeds with such shortcomings as asymmetric shape, small size, round shape, severe worm-eaten and serious damage were found and excluded. The corn seed color image was transtbrmed into saturation binary, image. If the target area of this binary image was far below the average area value of the standard corn seeds, the seed was considered with severe mildew. Slight black mihtew was judged according to the value of （R ＋ G ＋ B）/3 was snmll. Slight white mildew or slight damage was judged according to the value of B R was small. At last, the orientation of embryo, up or down, was detected according to the characteristics which the embryo of corn seed was close to white and it mainly located in the major axis. Of course, the direction of the tip point, left or right, determined the angle of of. Experiments show that this algorithm can detect the qualification and the direction of corn seeds quickly. The time of detection for one seed is about 14 ms. The accuracy rate of repeated corn seed detection is 95%. The accuracy rate of qualification detection is 96. 1%. The accuracy rate of embryo orientation detection is 97. 1%.