Abstract
Cereals are the main food for mankind. The grain shape extraction and filled/unfilled grain recognition are meaningful for crop breeding and genetic analysis. The conventional measuring method is mainly manual, which is inefficient, labor-intensive and subjective. Therefore, a novel method was proposed to extract the phenotypic traits of cereal grains based on point clouds. First, a structured light scanner was used to obtain the grains point cloud data. Then, the single grain segmentation was accomplished by image preprocessing, plane fitting, region growth clustering. The length, width, thickness, surface area and volume was calculated by the specified analysis algorithms for grain point cloud. To demonstrate this method, experimental materials included rice, wheat and corn were tested. Compared with manual measurement results, the average measurement error of grain length, width and thickness was 2.07%, 0.97%, 1.13%, and the average measurement efficiency was about 9.6 s per grain. In addition, the grain identification model was conducted with 25 grain phenotypic traits, using 6 machine learning methods. The results showed that the best accuracy for filled/unfilled grain classification was 90.184%.The best accuracy for indica and japonica identification was 99.950%, while for different varieties identification was only 47.252%. Therefore, this method was proved to be an efficient and effective way for crop research.
Highlights
Cereals are the main food for mankind
Our research demonstrated a novel method for grain 3D and plumpness information extraction with high throughput and high accuracy, which was definitely helpful to the rice breeding and genetic research
The accuracy of the error analysis result is evaluated by mean absolute percentage error (MAPE), root mean square error (RMSE) and determination coefficient ( R2 )
Summary
Cereals are the main food for mankind. The grain shape extraction and filled/unfilled grain recognition are meaningful for crop breeding and genetic analysis. A novel method was proposed to extract the phenotypic traits of cereal grains based on point clouds. The length, width, thickness, surface area and volume was calculated by the specified analysis algorithms for grain point cloud To demonstrate this method, experimental materials included rice, wheat and corn were tested. The results showed that the best accuracy for filled/unfilled grain classification was 90.184%.The best accuracy for indica and japonica identification was 99.950%, while for different varieties identification was only 47.252% This method was proved to be an efficient and effective way for crop research. Light imaging, an active three-dimensional vision technology, can obtain high-precision point clouds, which is widely used in industrial detection, reverse engineering and cultural relic p rotection[19], and it provides an effective method for high precision analysis of cereal grain 3D traits. It is urgent to develop a new method for the recognition of filled/unfilled grains, with high efficiency and low radiation risk
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