Rapid extraction technology of the root morphological characteristics via minirhizotron image
LIU Kai1, LI Wenbin1,2, ZHAO Yue1,2, HAN Qiaoling1,2, ZHANG Pengchong1, ZOU Songyan3, TAN Jianbiao3
1. The School of Technology, Beijing Forestry University, 100083, Beijing, China; 2. Key Laboratory of State Forestry Administration for Forestry Equipment and Automation, 100083, Beijing, China; 3. The College of Forestry, Beijing Forestry University, 100083, Beijing, China
Abstract：[Background] The morphology of roots and the distribution of soil layers reflect the growth state and utilization strategy of soil water-trophic resources,it has a great significance to improve the quality of plantation cultivation and soil and water conservation. However, the traditional root research methods, such as soil drill method, soil column method, have complex test processes and cause certain damages to the root system. Therefore, it is very important to propose a new experiment and research method that may achieve nondestructive, automatic and digital monitoring of root growth state.[Methods] Selecting 3 healthy trees(Populus tomentosa), designing the experiment based on the minirhizotron technology, and taking 14 days as the data collection period, the root distribution in different soil layers was obtained. Root image was clipped and the true value was obtained via manual annotation. After image filtering processing, the Otsu, K-means, and FCM(fuzzy C-means clustering algorithm) methods were used to segment images. The correct rate was selected as the quantitative analysis index, and the experiment results were obtained.[Results] Through the qualitative analysis, there was over-segmentation when Otsu method was used to have image segmentation for the two types of root, and over-segmentation and under segmentation for the K-means method. The segmentation results via FCM method for the two types of roots were similar to the one via manual annotation. Through quantitative analysis, correct rate by K-means and FCM method were similar, all better than that by Otsu method. The accuracy of FCM method reached 90%.[Conclusions] The experiment was designed and was in steady operation, and the changes of root systems were obtained under different soil layers, i.e., the nondestructive monitoring was achieved. Through image segmentation, the segmentation algorithm was obtained by which its average error rate was <10%, i.e., the automatic monitoring was achieved. The characteristic parameters such as total root length was taken as example, the root growth turnover under different drip irrigation strategies was analyzed, and showed good applicability, which offered an efficient and nondestructive approach for the root ecology research.
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2021, Vol. 19 
