College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China.
Sensors (Basel). 2022 Dec 20;23(1):20. doi: 10.3390/s23010020.
The detection of water changes in plant stems by non-destructive online methods has become a hot spot in studying the physiological activity of plant water. In this paper, the ultrasonic radio-frequency echo (RFID) technique was used to detect water changes in stems. An algorithm (improved hybrid differential Akaike's Information Criterion (AIC)) was proposed to automatically compute the position of the primary ultrasonic echo of stems, which is the key parameter of water changes in stems. This method overcame the inaccurate location of the primary echo, which was caused by the anisotropic ultrasound propagation and heterogeneous stems. First of all, the improved algorithm was analyzed and its accuracy was verified by a set of simulated signals. Then, a set of cutting samples from stems were taken for ultrasonic detection in the process of water absorption. The correlation between the moisture content of stems and ultrasonic velocities was computed with the algorithm. It was found that the average correlation coefficient of the two parameters reached about 0.98. Finally, living sunflowers with different soil moistures were subjected to ultrasonic detection from 9:00 to 18:00 in situ. The results showed that the soil moisture and the primary ultrasonic echo position had a positive correlation, especially from 12:00 to 18:00; the average coefficient was 0.92. Meanwhile, our results showed that the ultrasonic detection of sunflower stems with different soil moistures was significantly distinct. Therefore, the improved AIC algorithm provided a method to effectively compute the primary echo position of limbs to help detect water changes in stems in situ.
利用非破坏性在线方法检测植物茎部水分变化已成为研究植物水分生理活性的热点。本文利用超声射频回波（RFID）技术检测茎部水分变化。提出了一种算法（改进的混合差分 Akaike 信息准则（AIC）），自动计算茎部超声首波的位置，这是茎部水分变化的关键参数。该方法克服了由于各向异性超声传播和茎部不均匀性导致的首波位置不准确的问题。首先，对改进算法进行了分析，并通过一组模拟信号验证了其准确性。然后，对一组取自茎部的切割样本进行了超声检测，在吸水过程中计算了茎部含水率与超声速度之间的相关性。结果表明，这两个参数的平均相关系数达到约 0.98。最后，对不同土壤湿度的活体向日葵进行了原位超声检测，时间从 9:00 到 18:00。结果表明，土壤湿度与超声首波位置呈正相关，尤其是 12:00 到 18:00 之间，平均相关系数为 0.92。同时，我们的结果表明，不同土壤湿度下向日葵茎部的超声检测具有明显的差异。因此，改进的 AIC 算法为有效计算肢体的首波位置提供了一种方法，有助于原位检测茎部水分变化。
