Stem expansion and contraction are closely related to the plant water status. It is of great significance to real-time monitor the stem expansion and contraction, in order to improve the utilization rate of agricultural water resources in irrigation systems. Therefore, the sensors have been mainly used to monitor the expansion and contraction of plant stems at present. The linear micro-displacement can be measured to assess the plant water status. However, it is still limited to the high cost and inconvenient installation. In this study, a flexible and wearable sensor was designed and developed using the piezoresistive effect. A flexible pressure electrode was used as the sensing element to monitor the pressure variation, which was attached to the surface of plant stems. Different from the linear micro-displacement sensor, the developed sensor was used to assess the pressure variation caused by the expansion and contraction of the stem. The pressure signal detection and data collection were also designed to convert the pressure into electrical signals for storage. A series of experiments were conducted to evaluate the performance of the pressure sensor. Firstly, the performance test and sensor calibration were conducted under laboratory conditions. Then, the pressure sensor was installed on a tomato stem in a greenhouse, in order to observe the pressure variation. A comparison was made with the linear micro-displacement sensor. Finally, the variations in the expansion and contraction of tomato stems were observed under sufficient irrigation and water deficit using the two types of sensors. The results show that the average relative change rate of the stability test for flexible pressure sensors was 0.109%; There was negligible output variation caused by bending; The determination coefficient of calibration was greater than 0.99, and the most suitable working range was 2-100 kPa; The determination coefficient of the measurements between the two types of sensors was 0.955 1, compared with the linear micro displacement sensors. The greenhouse experiments show that the determination coefficients of the measurements using the two types of sensors were 0.767 2 and 0.851 9, respectively, for full irrigation and deficient irrigation. The flexible and wearable pressure sensor can be expected to monitor the variations in the expansion and contraction of tomato stems. The water deficit stress of the tomato can also be diagnosed as well. The pressure sensor achieved better performance during the experiment, such as the low cost and easy installation, compared with the linear micro-displacement sensors.

A root is one of the most important vegetative organs of plants. It is of great significance to explore the growth status of root under different water stress in modern agriculture. However, the existing observation of plant root are cumbersome, laborious and time-consuming. It is the high demand to meet the requirements of precision irrigation and water-saving under field conditions. In this study, a pipeline robot system was developed for the synchronous, in-situ and the high-throughput monitoring of plant root and root soil water using STM32. The system consisted of pipeline robot, data base station and PVC transparent pipeline. The pipeline robot system was embedded in the soil, where the macro camera and soil moisture sensor were carried by the robot. The root images were captured to obtain the soil moisture data while the robot cruising. Meanwhile, the robot shared the functions of autonomous timing cruise, wireless communication using command data and active return after encountering obstacles. The distorted correction, registered on plane, identified and segmented images were obtained for the parameters of the root area, length and density of the plants in the direction of the pipeline. The results of laboratory test show that: 1) The pipeline robot was clearly captured the root images, indicating the excellent performance on distortion correction and plane registration. The true length and area corresponding to a single pixel were 44 μm and 0.002 mm², respectively, whereas, the shooting range of a single root image was 14.17×10.60 mm. The characteristic information of root was obtained using MATLAB, where the images taken by the automatic cruise of pipeline robot. The image processing operations included the image distortion correction, image preprocessing, root region recognition and segmentation, and root feature extraction. Compared with the root characteristic parameters measured by the excavation , the relative errors of the root image processing program were 12.29%, 3.40% and 12.50%, respectively; 2) There was an excellent linear relationship between the output voltage of the soil moisture sensor that carried by the pipeline robot and the soil volumetric moisture content, where the coefficient of determination was 0.990; 3) The pipeline robot presented a high accuracy of autonomous cruise positioning, with a mean relative error of 1.47%. The field experiment show that: 1) The pipeline robot system was captured the plant root images with high-throughput in the field environment. Root growth dynamics was obtained to further extract the parameters of root length, area, average diameter and density from the images; 2) The pipeline robot was accurately monitor the soil moisture in the root zone, where the mean relative error of the measured was 2.23%, compared with the drying measurement; 3) Once the system was initially fully charged, the pipeline robot system operated independently for no less than 7 days, where the maximum cruise monitoring distance was about 48m. The pipeline robot system can be expected to realize in-situ and high-throughput measurement of plant root and soil moisture in the root zone under field environment. The growth of root can be extracted after image recognition and segmentation. The finding can also provide the technical support to monitor the growth status of root in water-saving irrigation.