Assessment of uniformity of corn seedling planting in transplanted using image processing

Cultivation of corn in the form of seedlings can be considered as a solution for producing early crops, reducing production time, and saving water and various agricultural inputs. Therefore, the aim of the current research was to develop an algorithm to evaluate the uniformity of planting in the nursery with the help of image processing. In order to determine the number of seedlings, various transformations (excessive greenness, normalized intensity difference index of red and green values ​​and normalized intensity difference index of blue and green values) were evaluated. Then, by applying thresholding by Etsu method, the information of corn seedlings was separated from the background. Finally, by implementing the Freeman labeling method and the Harris method and analyzing the features of overlapping areas, an automatic algorithm was developed to count corn seedlings in these areas. The developed algorithm was evaluated online at speeds of 5, 8, 10 and 12 km/h. According to the obtained results, the best uniformity and monoculture was obtained by the transplanter at a speed of 5 km/h with a percentage of correct cultivation of 97% and the highest percentage of incorrect cultivation at a speed of 12 km/h and equal to 21%. Therefore, based on the present study, according to the obtained accuracy, the developed algorithm can be used not only to count the number of corn seedlings in the field, but also to evaluate the uniformity of planting of seedlings.