Methods and experiments of obtaining corn population based on machine vision

Abstract: It is very important to count corn population for optimizing plant density of each corn variety, and corn population is also a very important parameter for calculating average yield of each corn plant. Generally speaking, there are three methods to count corn population, which are based on mechanism, photoelectric technology and machine vision separately. In order to decrease the labor intensity and improve the accuracy, image identifying technology is used in this paper to obtain corn population. As corn seedling and weeds have some similarities, and not every corn seedling can grow up to a ripe corn, counting ripe corn's population is more significant than counting corn seedling's population. But it is not easy to enter the ripe-corn field for machinery, additionally, corn leaves will overlap and be blown by even slight wind, which will disturb image obtaining. There are also some solutions for the problems mentioned above, for example, corn fields will have a big difference after being operated by high-stubble corn harvesters. A section (300 to 500 mm) of corn stalks will be retained in the field after being harvested by high-stubble corn harvester, and there will be a distinct comparison between the stubble cross-section' color and other sceneries in the image. Processing images obtained from these fields will decrease the difficulties and improve the accuracy. So image acquisition equipment is mounted on the high-stubble corn harvester. Actually, the visual document obtained from the field is video document at first, and then the video document is decompressed into image. Subsequently, the RGB (red, green, blue) images are converted to gray images for mosaicking. The gray images are converted to binary images in the image segmentation and border extraction section next to image mosaicking section. Although the cross-section of stubble is not a perfect circle, its edge has an obvious feature compared to other objects in the image. At last, a function is used to extract the edge of stubble cross-section, and then the centroid of cross-section is marked. So corn population can be obtained by counting the marks. Experiments were done to test the method and the design in this paper was in autumn of 2013. Experiment results have expressed that there is no significant difference (P<0.05) between artificial seeding and mechanical seeding; and there is also no significant difference (P<0.05) between automated counting and manual counting. The automated count's mean error is only 6.7%, and this error will not accumulate along with the increasing number of corn plant. The results of artificial count and automated count are linear correlation. The results of linear regression analysis show that the values of R2 of four experiments are 0.95, 0.90, 0.91 and 0.91, respectively, the slopes of four regression lines are 0.93, 0.91, 1.08 and 0.95 separately, and the intercept of four regression lines are 0.98, 0.97, -0.12 and 0.97 respectively. The design in this paper can reduce the difficulty in identifying corn stalks in images, and improve the image-identifying accuracy at the same time, and hence can better serve the real problems in counting corn population.