Abstract:

To obtain the phenotypes of rapeseed siliques more efficiently and accurately, a machine vision-based method for analyzing the phenotypes of rapeseed siliques was proposed using image processing technology and deep learning algorithms. Siliques of rapeseed cultivar Yingchun No. 1 were used as materials. The need to acquire morphological phenotypes of siliques is considered for rapeseed breeding. Image processing technology was used to extract the morphological phenotypes of the rapeseed siliques, including pedicel length, silique length, silique width, chord length, arc length, and area. YOLOv5 was used to perform nondestructive silique grain counting. Measurements and verifications were made on the siliques and calibration objects. There was no significant difference (P>0.05) between the phenotypic indexes of siliques assessed by image analysis and the actual measured values. The R²was more than 0.96, the root mean square error (RMSE) was less than 3 mm, the mean absolute error (MAE) was less than 2.80 mm, and the mean absolute percentage error (MAPE) was less than 4%. The calibration object diameter had a maximum RMSE of 0.3 mm, the MAE was less than 0.28 mm, and the MAPE was less than 2.00%. The area index had a maximum RMSE of 12.09 mm², the MAE was less than 11.56 mm², and the MAPE was less than 5%. There was no significant difference between the number of grains identified by YOLOv5 and the actual value (P>0.05), R²was 0.99, RMSE was 0.68, MAE was 0.27, and MAPE was 1%. The method for analyzing the phenotypes of rapeseed siliques proposed in this study is easy to operate and labor-saving. It can effectively reduce the manual measurement error, improve the reliability of obtaining phenotypic information, and increase the efficiency of rapeseed breeding work. It also provides particular guidance for the quantitative analysis of rapeseed phenotypic information.

Key words: rapeseed silique, phenotype, image processing, grain counting