High throughput automatic extraction method of corn ear parameters based on multiple cameras images

Abstract: The efficiency and accuracy of corn ear test are two of the key factors restricting the breeding efficiency seriously. Corn ear test includes the measurement, records, statistics and analysis of parameters such as ear weight, ear length, ear width, number of ear rows, kernels per row, average thickness of kernel, kernels per ear. In this paper, a corn ear parameter extraction method based on 4 cameras was proposed based on the high-throughput automatic measuring device which has been developed previously. Four high-resolution color cameras were evenly distributed around the ear with the interval of 90° to get the corn ear images from 4 directions at the same time. Every image from the corresponding camera was processed including image preprocessing, projection model building, and parameters extraction of corn ear. During image preprocessing process, center part of the original image with the length of 7/9 of the original image length, the width of 1/2 of the original image width was chosen as the processed area. Binarization processing was applied to the area to obtain binary image, and the binary image was processed by image denoising, hole filling and other morphological transform. An AND-operation was then applied between the processing result and the original image to access the corn ear images without background. The projection model was constructed after image preprocessing process, which considered ear cross-section circular, and kernels were distributed on ear cross-section as point on the circumference of a circle. Thus, number of ear rows can be easily calculated according to the relationship between number of ear rows and circumferential angle of those rows. Procedure such as kernels area acquisition, kernels center position acquisition, kernels at edge removal, reserved kernels tracking and corn ear parameters calculation are operated based on the projection model. Since there are 4 images for each ear, the final ear parameters including ear length, ear width, average thickness of kernel, number of ear rows, kernels per row, kernels per ear are calculated based on parameters measured from each image. The ear length and width are represented by the maximum length and width of the smallest external rectangle of the 4 images. Number of ear rows in each image is calculated from the valid row number and the circumferential angle which can be obtained on the basis of the projection model. Kernels per row are acquired by tracking the kernel area for each ear image, the maximum number of kernels in a row for each image is calculated as well as the average value, and the round-of number is considered as kernels per row of the ear. Kernels per ear are calculated from the valid row number, kernel number of the valid rows and corn ear rows. Average thickness of kernel is calculated according to the tracked kernel number and the total tracking path. Experiments are carried out with the high-throughput automatic measuring device for corn, and results show that the determination coefficients (R2) of ear length, ear width and average thickness of kernel achieve 0.997 3, 0.984 and 0.941 5 respectively between the values obtained by the proposed method in this paper and that measured artificially. The measuring accuracies of number of ear rows and kernels per ear are 98.63% and 95.35%, respectively, which meet the requirements of corn parameters measurement during maize breeding. The proposed method also provides a new train of thought for the extraction of corn ear parameter, and it also lays a solid foundation for the realization of automatic high-throughput device for corn.