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A long-term in situ measurement cannot fully meet the requirements of soil nitrate measurement in recent years, due to the high cost and the inability. In this study, a measurement method was proposed to collect the soil solution using the titanium sintering filter cartridge, in order to detect the nitrate concentration in the soil solution under near-infrared spectroscopy. Correspondingly, the detection device was also designed in this case. Therefore, it was demonstrated that the nitrate was suitable for the analysis at 1 250 to 1 860 nm. The commercially available Near-Infrared (NIR) Light-Emitting Diode (LED) products were combined with the 940, 1 050, 1 200, 1 310, 1 350, 1 450, and 1 550 nm NIR LED. Simulated experiments of soil solution collection were carried out using self-made soil columns. Titanium sintering filter cartridge was selected as the soil solution sampler to collect the soil solution. The results show that the soil solution collection was positively correlated with the water content of the soil, but negatively correlated with the burial depth and the clay content of the soil. It infers that the titanium sintered cartridge was suitable for sandy or clayey soils with low clay content. Once the soil moisture content exceeded 20 %, the collection time was selected as 30 min. But the collection time needed to be extended, when the moisture content was low. A systematic evaluation was made to consider the volume of solution required for the test chamber and the vertical distribution characteristics of soil nitrogen. More importantly, the optimal collection time of the soil solution was required over 60 min, where the burial depth was 10 to 40 cm. In addition, the comparative tests confirmed that the titanium sintered cartridge presented a greater capacity for soil solution collection than the LLYQ-P02 soil solution sampler, suitable for lower soil moisture contents. Finally, the BP neural network was trained using a Bayesian regularisation, with the voltage data at different wavelengths as the input layer and solution nitrate-nitrogen content as the output layer. Specifically, 70 % of all data were randomly used as the training set and the remaining 30 % as the validation set to complete the training of the nitrate-nitrogen prediction model. The feasibility and stability of the detection device were tested through the performance tests (including stability, resolution, dynamic response, and field comparison tests). The results show that the voltage tended to increase with the increasing nitrate concentration, while the voltage was lower at 1 310, 1 450 and 1 550 nm. Therefore, the presence of a large absorbance confirmed that the absorption at 1 310 and 1 550 nm was influenced by the nitrate and at 1 450 nm by water. The Pearson's correlation coefficients were 0.997 and 0.995 for the training and test set of the nitrate nitrogen prediction model, respectively. The root mean square error of prediction was 3.43 during this time. The improved prediction model of nitrate nitrogen performed the best prediction than before. The relative deviations of the measuring device from the ion electrode and the colourimetric were 5.9% max. and the absolute deviation was 9 mg/L max., respectively, fully meeting the requirements for the in-situ monitoring of nitrate nitrogen in soil solution. The new device can serve as fully automatic, non-destructive (no interference with the original soil structure), long-term, and in-situ, not available with the current nitrate ion electrodes and soil nutrient quick testers. An optimal combination of operational performance was achieved, where the standard deviation of the output voltage was 0.006, and the dynamic response time for a single wavelength was 1.4 s during the long-term operation of the detection device. The finding can provide a feasible solution for the long-term automatic measurement of soil nitrate nitrogen and the construction of a water-fertilizer integration system. [ABSTRACT FROM AUTHOR]

The prevention and control of saline soil is of great significance to the sustainable development of agriculture in Xinjiang, and comprehensive knowledge of the Salt content of saline soil is a prerequisite for the prevention and control of salinization, so it is necessary to find an accurate and rapid method to estimate salt content information of saline soil. Ground Penetrating Radar (GPR) is widely used in geophysical and environmental assessment as a non-destructive detection technology. In recent years, this technology focuses on the detection of water content. However, the detection of soil salt is relatively weak due to the large dielectric loss in saline-alkali soil, the easily-absorbed electromagnetic wave energy the complex way to process radar signal, and even the different frequency combinations are needed to test, all of which above bring great troubles to the detection of salt. However, the rapid development of dielectric models in salinized soil makes it possible to use this technique to retrieve salt content. In order to better understand the soil salt content in drip irrigation cotton field, a typical drip irrigation cotton field was selected as the research object to be detected with a Canada's Ekko PRO series GPR multi-frequency antenna (250 MHz and 1 000 MHz). Then, the soil dielectric constant was obtained through radar amplitude envelope Average method (AEA). Soil salt content was estimated by adopting the Dobson saline soil dielectric model (Dobson dielectric model of saline soil, Dobson) as the theoretical tools. Meanwhile, the dielectric constant, soil bulk density, water content, and soil clay content were applied as the input variables of the model; Multiple Linear Regression (MLR) method was used to establish soil salinity inversion model; BP neural network (BP) was employed for simulation and prediction. Finally, the accuracy of salt content inversion by MLR model, BP model and Dobson model was evaluated based on the measured salt content. The results showed that: (l) The effective depth of GPR 250 MHz and 1000 MHz frequency antenna AEA method was 0-30 cm. (2) The dielectric constant obtained by 1 000 MHz frequency antenna AEA had a good polynomial relationship with the measured water content, and the fitting effect and accuracy of the measured water content and the inverse water content were good (R², RMSE, MER were 0.96, 1.61%, and 7.25%, respectively). (3) Among the three salt inversion models, the inversion accuracy of Dobson salinized soil dielectric model was significantly higher than that of the other two methods, with R² and RMSE of 0.91 and 0.313 g/kg respectively. Therefore, it is suitable and reliable to use GPR multi-frequency antenna AEA method to estimate soil salt content in cotton fields under drip irrigation. This method provides a new way to retrieve soil salt content and enriches the methods and means of detecting soil salt content. [ABSTRACT FROM AUTHOR]