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.

Drought stress of crops is an important factor affecting their yield and sustainable agricultural development. Accurate diagnosis of crop drought stress is the basis for improving water resource utilization. Imaging technology can quickly, automatically, non-destructively, accurately acquire and analyze the phenotype characteristics of crops, providing a powerful new tool for crop science research. This paper focuses on the review of phenotype imaging analysis techniques for crop drought stress diagnosis. First, the single imaging technique for crop drought stress diagnosis was introduced, and then we introduced the fusion imaging technique for crop drought stress diagnosis. In the aspect of single imaging technology introduction, firstly, the principles of six phenotype imaging techniques including RGB imaging, 3D imaging, near-infrared imaging, hyperspectral imaging, chlorophyll fluorescence imaging and thermal imaging are introduced in this paper, and then we introduced research progress of the single imaging technology in crop drought stress phenotype analysis, beside the research achievements in crop drought stress in recent years were further summarized. Finally, the imaging technology was summarized and prospected at the end of each imaging technology introduction. In the fusion imaging technology of crop drought stress, this paper first summarized the research results of the automatic comprehensive phenotype imaging analysis platform of crop drought stress in recent years, and then summarized and analyzed the research of different fusion imaging methods, analyzed their advantages and disadvantages, and prospected the future research direction of fusion imaging technology in the end. With the horizontal and vertical comparative analysis of the research results of a single imaging technology, we found that RGB imaging technology has the lowest application cost and the most extensive application range, but the lowest accuracy. The 3D imaging solves the problem of crop occlusion in RGB imaging, improves the accuracy, and is widely used in high-throughput phenotype extraction platforms. The information on crop phenotype parameters can be obtained by near-infrared spectroscopy, chlorophyll fluorescence and hyperspectral imaging in a fast and non-destructive way. Among them, the application scope of NIR imaging is limited due to its limited ability to obtain phenotype information and relatively high cost. Chlorophyll fluorescence and hyperspectral imaging are better at obtaining physiological and biochemical parameters of crops, and are widely used in the fusion of imaging methods. Thermal images obtained by infrared thermal imaging are often used to obtain crop physiological parameters, and are also combined with visible light images for phenotype extraction. In the meantime, the method of obtaining crop phenotype by using the fusion of multiple imaging technologies has the advantages of different imaging technologies, which can effectively avoid the defects of a single imaging technology and make up for the deficiencies of obtaining single imaging phenotype parameters, so as to reflect the crop growth status more accurately and efficiently. More accurate feature extraction can be achieved by integrating various image information obtained by various imaging technologies and using artificial intelligence methods for integrated image processing. The use of fusion imaging technology to obtain crop phenotypes will be one of the important directions of crop drought stress phenotypes extraction in the future. Finally, according to the current development situation, future research on phenotypic imaging technology for crop drought stress diagnosis prospects, including the development of new devices and the combination of new artificial intelligence algorithms.

Fruit trees can often suffer from the frost damage at low temperatures. The fruit tree stems can be gradually frozen to disrupt the internal water transport after cork formation. Effective identification of frost damage is required for early protective measures to reduce the losses in the yield of fruit trees. Commonly-used techniques can be used to detect the internal freezing and thawing in trees, such as calorimetry, ultrasonic emission, dielectric , nuclear magnetic resonance imaging, and X-ray imaging. However, only electrical impedance tomography (EIT) can reflect the impedance distribution in the image form, thus revealing the internal changes during freezing and thawing of fruit tree. In this study, the electrical impedance imaging was proposed to identify the freezing and thawing state of fruit tree stem. A 16-electrode EIT system was designed to connect the hardware in a stacked form, such as the power supply, signal generation, voltage-current conversion, multiplexing, signal demodulation, control unit, and temperature acquisition. In power supply module, various voltage regulators and boost converters were employed to provide the different positive and negative power supplies. While the signal generation module was used to output the stable sinusoidal voltage signals with adjustable frequency. The constant current source was utilized to serve as the enhanced Howland current source. The multiplexing module was used to switch among 16 electrode channels. The signal demodulation module was used to obtain the imaging data, and then demodulate the amplitude and phase of the measurement signal. The accuracy of this process was dominated the imaging. Analog demodulation was then chosen for this design. The control unit module was acquired and then stored the raw voltage data from EIT measurement. The wireless connections were also established with the host computer system via serial communication and Bluetooth modules. Temperature acquisition was integrated into the system as an essential part of freezing and thawing experiments. The system software included a Windows-based host computer system developed in C#. Control and monitoring of the hardware system were realized to determine the experiment parameters, and then record the data. The open-source EIDORS package was also incorporated to solve the imaging forward and inverse problems. System evaluation tests were carried out to clarify the performance and measurement, including constant current source output impedance, channel consistency, temperature correction, imaging, and measurement range. The output impedance of the constant current source was also tested after evaluation. The simulation results showed that the output impedance at 0-100 kHz was in the MΩ level. The measured impedance failed to reach the simulated value with the increasing frequency, but it was close to the MΩ level in the low frequency range. Three datasets of U-shaped curve were consistent after simulation, indicating the better consistency of channel. Furthermore, the standard deviation of the output amplitude significantly decreased after temperature correction. Differential imaging experiments were carried out on the cylindrical bodies with circular, triangular, and square insulation. Better imaging was achieved in the circular bodies, with the slight distortion at the edges of triangular and square bodies, yet essentially matching the shapes. The better performance of imaging was obtained on the distribution of hollow gypsum moisture with a diameter of 3 cm. Since the water content was generally lower than that of gypsum for actual stem objects, the actual measurement was less superior to the simulated gypsum objects. Therefore, the measurement object was tentatively set to small stems below 3 cm in diameter. Laboratory experiments on fruit tree stems confirmed that the conductivity was attributed to the great variation in the ice water content during stem freezing and thawing. The temperature gradually decreased over time during freezing. Among them, a latent heat process where the temperature remained almost constant, often corresponded when the water inside the stem converting to ice. The average conductivity also gradually decreased over time. In thawing, the temperature gradually increased over time, with a latent heat process corresponded when ice inside the stem converting to water. The average conductivity also gradually increased over time. Specifically, both ice content and overall impedance increased during freezing, whereas, the conductivity decreased. Both the ice content and overall impedance decreased during thawing, whereas, the conductivity increased. The freezing and thawing of the stem were observed using time-difference imaging of moisture distribution. The regions with the lower conductivity was continuously expanded during freezing, corresponding to the decrease in liquid water content. Conversely, the regions with the lower conductivity was gradually shrunk during thawing, corresponding to the increase in water content within the stem. Both processes were consistent with the average conductivity. The impedance imaging system can provide the better experimental performance to identify the freezing and thawing state of stems with a diameter of 3 cm.

The soil water characteristic curve is one of the most crucial indicators in soil water dynamics and irrigation. However, the current measurement systems of soil water characteristic curves still had some challenges, such as the switching errors among multiple sensor probes, high costs, and limited fixed-points difficult in the field. In this study, an in-situ scanning system was designed to monitor the soil water at multiple depths in farmland using the frequency domain dielectric spectroscopy. The volumetric water content and matric potential data were collected from different depths. The scanning system also comprised a dielectric tube sensor module (including two sensors for the moisture and matric potential), a main control, a 4G communication, solar power, and an Alibaba Cloud monitoring module. The dielectric tubes were vertically installed in the soil to be tested. Two sensors were then utilized to scan and monitor the soil moisture and matric potential at various depths (5-60 cm, at 5 cm intervals). The data was collected to store locally on an SD card and then uploaded simultaneously into the cloud platform for remote monitoring. In system installation, the small-diameter drill bits (25 mm for the volumetric water content tube, and 85 mm for the matric potential tube) were used to bore holes for the dielectric tubes into the soil. A series of measurements were carried out in the various total depths, sensor probe intervals, and cycles. The low consumption of the system was obtained with the standby power at 0.62 W and working power at 2.4 W. Each operational cycle was about 2 min, with an hourly power consumption of 0.68 W·h. The capacity of the solar panel battery was 3×104 mAh with a rated voltage of 12 V. A long-term monitoring was realized in the field under adequate sunlight. The effective response radius of the sensors was determined to incrementally increase the soil layer height until the sensor output stabilized. The thickness of gypsum was 30 mm to measure the matric potential. The sensors were calibrated for the volumetric water content using soil samples with different water contents. A determination coefficient of 0.991 was achieved in the calibration curve. A commercial matric potential sensor (TEROS-21) was used to calibrate the soil matric potential. The determination coefficient of 0.989 was observed in the calibration curve, indicating excellent consistency within the measurement range. Field trials of the system were conducted in a demonstration field of returning green winter wheat. A total depth of 60 cm was measured with intervals and cycles of 5 cm and 1 h, respectively. The results indicate that the scanning system can accurately monitor the dynamic changes in the soil profile volumetric water content and matric potential within the root zone of winter wheat. The water characteristic curve of the soil profile showed that the water consumption in the 0-20 cm layer was attributed to both root absorption and surface soil evaporation. The main root absorption area of winter wheat in the regreening period was 25-45 cm, while the 50-60 cm layer with fewer roots maintained the higher soil water content. In-situ determination of soil profile moisture characteristic curves can be expected to assess the soil water retention capacity. The finding can also provide essential data and technical support for the soil moisture in the crop root zones during intelligent water-saving irrigation.