Assessment of uniformity of corn seedling planting in transplanted using image processing
2022
Abdanan Mehdizadeh,, Saman | Orak,, Hadi | Kazemi Karaji, Fateme
AbstractCultivation of corn as a seedling could be considered as a way to produce an early crop, reduce production time and also save water and agricultural inputs. Therefore, the aim of the present study is developing an algorithm for evaluation of the planting uniformity in transplanting using image processing. In order to determine the number of seedlings, different transformation methods (Excessive green, green and red normalized difference index and green and blue normalized difference index) were evaluated. Then, the corn seedlings information was taken out from the circumstantial information using Otsu’s method. Finally, by applying Freeman labeling and Harris method and analyzing the characteristic parameters of the overlapping regions of the fields, an automatic counting of corn seedlings in the overlapping regions was established. The developed algorithm was evaluated online at speeds 5, 8, 10 and 12 km / h. According to the obtained results, the best uniformity and single transplanting were achieved at a speed of 5 km / h with an accuracy of 97%. Furthermore, the highest percentage of incorrect planting was obtained 21% at a speed of 12 km / h. Therefore, the study demonstrated that the newly developed algorithm is reliable not only for automatic corn seedlings counting in the corn field but also for the uniformity evaluation of the transplanter.
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