Non-destructive detection of wheat tiller morphological traits based on X-ray CT technology

Abstract: Wheat tillers play an important role for nutrition transport to support the wheat growth. The wheat stem diameter and thickness are closely related to lodging resistance. Meanwhile tiller number and tiller angle directly determine the plant type of wheat. Therefore, the morphological trait extraction of wheat tillers is very important to the study of wheat genetics research, breeding improvement and functional genes location. With the development of the wheat cultivation and genetic breeding, the fast and accurate measurement of morphological traits for wheat tillers is imperative. However, the traditional method for tiller trait measurement is still manual, which is destructive and time consuming. Although a lot of efforts had been made to extract the tillers traits generally based on visible light, it is not able to acquire the inner information of wheat tiller and is affected seriously by tillers overlap. To solve the problem, a nondestructive technology for wheat tillers measurement was proposed and equipped with X-ray CT imaging device. In this study, the X-ray CT imaging system was constructed with the Micro-focus X-ray source and flat detector, which was used to obtain the sinogram images of wheat tiller with the spatial resolution 61 μm by 61 μm, and totally 360 images were collected for every one degree rotation for each plant. Then the FBP and GPU algorithms were adopted to reconstruct the tomography image of wheat tillers based on the sinogram images，and the inner information of wheat tiller was visible in the image. Moreover, the specialized image analysis algorithms were designed to analyze the wheat tomography image, in which the algorithms of background subtraction, OTSU segmentation, removing small region, and connected region identification were applied to extract the tiller regions. After that, the wheat tiller morphological traits were extracted by the following methods, the tiller numbers were counted based on the number of connected areas, the stem diameter was computed by the information of area external rectangle, the tiller wall thickness was extracted with the information of area external rectangle and cavity rectangle, and tiller angle was obtained by the triangle relation of tomography images at different heights. Finally this method was evaluated by 107 wheat plants, which belonged to five different wheat varieties. After the wheat plants were measured by the system automatically, the plants were measured by manual method for comparison to evaluate the system measurement accuracy. The experimental results showed that the system measurement accuracy of the tiller number was 100%, the mean absolute percentage error of tiller angle, the stem diameter and the stem wall thickness were 3.65%，4.84% and 7.86%, respectively and the RMSE for above traits were 2.96, 0.17 mm, 0.12 mm, respectively . The R2 value of tiller angle, the stem diameter and the stem wall thickness were 0.77, 0.91 and 0.87, respectively. The results demonstrated that this method had a good consistency with manual method, and performed a high accuracy for wheat tiller morphological trait measurements. In this study, the image acquisition efficiency was about 200 s per plant and the time used for image analysis was about 120 s per plant. Considering the parallel implement of image acquisition and analysis, the system efficiency was about 200 s per plant and was able to measure approximate 432 wheat plants in one day. Compared with manual method, this technology was able to detect the internal information of wheat tiller with high-accuracy and nondestructive. Moreover, it was able to extract novel phenotypic traits, which may contribute to the functional genomics and lodging resistance research of wheat plants. In future, more detailed information of wheat tiller such as vascular bundle, leaf sheath could be analyzed based on the higher resolution X-ray imaging device and more intelligent algorithms.