Abstract:
Accurate and rapid acquisition of maize seedling number plays an important role in early decision-making of breeding. The aim of this work was to use the UAV images collected in Xiaotang mountain, Beijing in June 2017, to recognize and extract the number of maize at seedling stage by establishing a set of digital morphological process. The UAV was flying at a height of about 40 m, and the image data were collected under clear and wind-free conditions. Four color space transformation models (RGB, HSV, YCbCr and L*A*B) were compared and analyzed. The model which showed minimum noise and maintains more pixels was selected to divide image. The foreground (crop) and background (soil) were separated to obtain binary graph. Based on the image scale transformation principle, the images were divided into two types according to the number of leaves. According to the experimental design requirements, different maize breeding materials were growing naturally under the same water and fertilizer conditions. However, it was difficult to control the growth situation of the material area accurately, so there was obvious growth difference between different varieties. When the UAV digital image data were processed, the number of plants was extracted at the same scale, but the result was not ideal, and the recognition rate was only 90.6%. In order to improve the recognition rate, maize breeding materials were classified into two leaf type and multi leaf type according to different growth potential. The skeleton extraction algorithm and multiple deburring processes were utilized to extract crop shape skeleton with high accuracy. Finally, the corner detection results of Harris, Moravec and Fast were compared regarding recognition rate, the leakage recognition rate, the error recognition rate and the operation efficiency. Finally, the Harris corner detection algorithm was used to better extract the number of the maize seedling. Considering that there would be some overlapping leaves in the images, this study showed a comparison between computer identification and actual plant growth. The results showed that this method was still reliable under leaf overlapping conditions. At the same time, the possible errors were analyzed: 1) the influence of the wind during UAV flight; 2) the impact of weeds in the field; 3) the wrongful identification of a single plant as two, but the overall accuracy was still reliable. Through accuracy verification, the leaf recognition rate reached 96.3%, the multi-leaf recognition rate reached 99%, and the overall recognition rate was 97.8%, which proved that it was feasible and reliable to extract corn seedlings from UAV images. This research adopted the principle of mathematical morphology, obtained the binary image by HSV color space transform, recognized and extracted the maize seedling morphological information of UAV images, removed most of the material in the cell skeleton recognition accuracy optimization noise by using image zoom scale transform. By using image zoom scale transform, the recognition accuracy was greatly improved, directly distinguished the specific number of maize materials within the UAV image. This method saved the manpower and material resources and provided strong support for the field of large area determination of germination rate and final yield.