基于机器视觉的蔬菜种子活力指数检测算法研究及系统实现

Study on vegetable seed vigor index detection algorithm and system realization based on machine vision

常规的种子活力检测存在主观性强、花费高、人工操作难等不足,无法满足当前自动化育种的发展需求。为了提高种子活力检测的自动化水平和质量,以辣椒种子图像为研究对象,利用创新的过黄特征法将种子图像灰度化,此方法可充分保留种子信息,通过阈值处理,可降低系统去除噪声所需的处理时间,为了标记每粒种子,采用漫水填充算法将种子填充为不同的灰度值;编程计算了种子发芽指数和种子平均根长,最终得出种子活力指数计算结果。通过不同种子图像的处理与试验,表明该算法在种子分离、活力指数计算方面具有一定优势,可适用于不同种子活力评估;对系统的评估精度进行试验测试,结果显示,自主开发的种子活力指数检测系统计算的种子活力指数准确率高达92%以上,可为蔬菜种子在线化生产提供参考。

Conventional vigor tests couldn＇t meet current demand for the development of automatic breeding because of their subjectivity, high cost and difficult operations. In order to improve the automatic level and quality of seed vigor detection, chili seed images were used to study the detection method. The innovative gray transformation was used to process seed-color images. This method could protect seed information enough. Combining self-developed system characteristics, threshold processing was used to remove noise, which could reduce the time of system pro- cessing seed images. For marking every seed, the paper used cvFloodFill algorithm to fill seeds into different gray values. Then the seed germination index and average seed radicle length could be acquired by self-developed soft-ware. Finally we combined system hardware and software perfectly. Through processing different seed images, the re- suh showed the system algorithm had certain advantages in the processing of such images. For assessing precision of system through calculalion of chili seed vigor index, the results showed that the accuracy of seed vigor index detection was grea/l） high. surpass 92%. So this system could provide researeh foundation for online produclion of vegetable seeds,