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Fast and precisely estimating total nitrogen (TN) content in soil helps to promote carrying out prescription fertilization. And soil moisture is a severe interference factor in forecasting soil nitrogen content based on real-time NIR spectroscopy. This paper aims at predicting soil nitrogen content based on real-time soil spectrum through exploring pretreatment method without artificial drying and sieving soil samples. Firstly, the real-time near infrared absorbance spectra of soil samples were measured and their characteristics were analyzed. Then 1st–7th level wavelet decompositions were carried out for each soil sample’s real-time spectrum. RSNR (Relative Signal-to-Noise Ratio) was constructed to evaluate wavelet filtering quality at different levels, and the results indicated that low-frequency signals obtained after the 3rd level wavelet decomposing had the best performance. And then 5 soil sample groups (each group had the same moisture content but different nitrogen contents) were selected and continuum-removal method was used for processing their filtering signals. And by using the methods combined wavelet analysis and continuum removal technology, six sensitive wavebands were determined for predicting the TN content in soil, which were 1375 nm, 1520 nm, 1861 nm, 2100 nm, 2286 nm and 2387 nm. Finally the real-time TN content detecting models were calibrated and validated based on PLSR (Partial Least Squares Regression) and SVM (Support Vectors Machine) respectively. For the PLSR model, its calibration R 2 was 0.602 and its RMSEC was 0.051 mg/Kg; the validation R 2 was 0.634, the RMSEP was 0.056 mg/Kg and its RPD = 1.838. For the SVM model, its calibration R 2 reached to 0.823, the RMSEC was 0.034 mg/Kg, the validation R 2 reached to 0.810, the RMSEP was 0.053 mg/Kg and its RPD was 2.129. It showed that, by using the proposed approach in this paper, the interference of soil moisture was mostly removed from soil real-time spectrum in the process of soil total nitrogen prediction, and the TN content regression models established by using the six sensitive wavebands had great performances in predicting soil TN content in real time. [ABSTRACT FROM AUTHOR]

Applying near infrared reflectance spectroscopy (NIRS) on farmlands can effectively estimate the total nitrogen (TN) content of soil online. We developed a NIRS-based portable detector of soil TN content that measures spectral data at 940, 1050, 1100, 1200, 1300, 1450, and 1550 nm. The soil spectral data are sensitive to external environmental conditions, particularly soil moisture content and particle size. The interference of these factors on predicting soil TN content must be eliminated when using the portable detector. First, soil samples were collected from a farm in Beijing, China, and scanned using the detector to obtain their absorbance data under varying soil moisture and particle size. Second, absorbance correction method and mixed calibration set method were proposed to correct the original spectral data and to eliminate the interference of soil moisture and particle size, respectively. The absorbance of the soil sample at 1450 nm exhibited a high correlation with soil moisture content. Thus, a moisture absorbance correction method (PMAI) was proposed to normalize the original spectral data into the standard spectral data and consequently eliminate the interference of soil moisture. A NIRS-based mixed calibration set based on the additivity of NIR spectra was produced with varying particle sizes, separated from the original soil samples, to eliminate the interference of soil particle size on the measurements of the portable soil TN detector. An estimation model of soil TN content was established based on the corrected absorbance data at six wavelengths (940, 1050, 1100, 1200, 1300, and 1550 nm) using an algorithm of the back propagation neural network. The correlation coefficient of calibration, correlation coefficient of validation, root mean square error of calibration, root mean square error of prediction, and residual prediction deviation were used to evaluate the model. Compared with the model used the original spectral data, the accuracy and stability of the new model were significantly improved. These methods could efficiently eliminate the interference of soil moisture and particle size on predicting soil TN content. [ABSTRACT FROM AUTHOR]

Abstract: In order to accurately understand soil water deficit and therefore execute effective and water-saving irrigation, the distribution map of soil moisture was obtained by the integration of wireless sensor networks (WSN) with spatial analysis software. The wireless nodes with moisture sensors were located at predetermined locations, and the geographical coordinates of these points were measured with a GPS receiver. The system sent data of soil moisture every 30 min to the remote management platform using the TCP–IP standard protocol. During the experimental period, the data of soil moisture were dynamically recorded, and the temporal and spatial variability were analyzed to decide whether irrigation was needed. Furthermore, in order to draw the distribution map of soil moisture with the Kriging interpolation method, a normal probability plot was created to test normality of the data, and the variogram model was built to check the spatial continuity of data. Then the distribution map of soil moisture was created with the Kriging interpolation method. Through a long-term testing in the field and data analyzing, it was proved that the whole system worked stably and reliably. The temporal curve could reflect the soil moisture changing trend during the experimental period, and the distribution map could be used to guide precision irrigation management. [Copyright &y& Elsevier]

Rapid and accurate measurement of high-resolution soil total nitrogen (TN) information can promote variable rate fertilization, protect the environment, and ensure crop yields. Many scholars focus on exploring the rapid TN detection methods and corresponding soil sensors based on spectral technology. However, soil spectra are easily disturbed by many factors, especially soil moisture and particle size. Real-time elimination of the interferences of these factors is necessary to improve the accuracy and efficiency of measuring TN concentration in farmlands. Although, many methods can be used to eliminate soil moisture and particle size effects on the estimation of soil parameters using continuum spectra. However, the discrete NIR spectral band data can be completely different in the band attribution with continuum spectra, that is, it does not have continuity in the sense of spectra. Thus, relevant elimination methods of soil moisture and particle size effects on continuum spectra do not apply to the discrete NIR spectral band data. To solve this problem, in this study, moisture absorption correction index (MACI) and particle size correction index (PSCI) methods were proposed to eliminate the interferences of soil moisture and particle size, respectively. Soil moisture interference was decreased by normalizing the original spectral band data into standard spectral band data, on the basis of the strong soil moisture absorption band at 1450 nm. For the PSCI method, characteristic bands of soil particle size were identified to be 1361 and 1870 nm firstly. Next, normalized index Np, which calculated wavelengths of 1631 and 1870 nm, was proposed to eliminate soil particle size interference on discrete NIR spectral band data. Finally, a new coupled elimination method of soil moisture and particle size interferences on predicting TN concentration through discrete NIR spectral band data was proposed and evaluated. The six discrete spectral bands (1070, 1130, 1245, 1375, 1550, and 1680 nm) used in the on-the-go detector of TN concentration were selected to verify the new method. Field tests showed that the new coupled method had good effects on eliminating interferences of soil moisture and soil particle size. [ABSTRACT FROM AUTHOR]

Highlights • An In-situ STN-SM detector was developed using laser light source. • Laser source had the better stability and light intensity than the LED light source. • The performance test showed that in-situ detector had good stability and reliability. • The detection accuracy of in-situ detector embedded SVM algorithm had been improved. Abstract In order to accurately acquire soil total nitrogen (STN) content and soil moisture (SM) content, an in-situ STN-SM content detector was developed, and the stability and accuracy of the detector were tested. The in-situ detector consisted of an optical unit, a control unit and a mechanical unit. The optical unit comprised eight single band Near-infrared (NIR) laser sources at 1260, 1330, 1360, 1430, 1530, 1580, 1660 and 1450 nm, collimator and an InGaAs photoelectric sensor, a laser source holder with "Eight faces" umbrella structure and sapphire glass. The control unit was composed of hardware part and software part. The hardware part included I-U conversion circuit, amplifier and filter circuit, and JN5139 module responsible for A/D conversion and data transmission. The software part of the program was written in the JN5139 microcontroller unit. The mechanical unit comprised a cylindrical detection darkroom, an electrical component mounting tray and a cylindrical shading and dust cover. The stability test results showed that the repeatability error at eight sensitive bands were 0.7758%, 0.8091%, 0.7958%, 0.5189%, 0.6405%, 0.8556%, 0.7391% and 0.5294%, respectively. The correlation coefficients between the absorbance of in-situ detector and MATRIX-I Fourier transform near-infrared spectrophotometer were all above 0.88. A portable LED STN content detector was taken as the reference in the performance test. The results showed that the calibration coefficient R c 2 and the validation coefficient R v 2 of in-situ detector model were 0.8322 and 0.7832, respectively. While those of the portable LED STN content detector model were 0.7205 and 0.6851, respectively. Two regression models were established for the SM content by respectively using the portable LED STN content detector and the in-situ detector, and the results showed that both detectors had good detection effects in SM. The experimental results showed that compared with the portable LED STN content detector, in-situ detector greatly improved the STN content detection precision, and could realize STN and SM content field detection in real-time. [ABSTRACT FROM AUTHOR]

• A soil surface image occlusion removal method is proposed. • Models for estimating soil moisture content in laboratory and field are established. • The vehicle-mounted terminal can effectively remove occlusions of images in the farmland. • Occlusion removal can improve the accuracy of soil moisture content estimation. • The practical application of image inpainting in farmland. In smart agriculture, soil images are frequently used to estimate soil moisture content (SMC). The soil image can't fully reflect the real soil surface condition due to the interference of weeds and other obstructions. To solve the occlusion problem, a new occlusion removal idea is proposed: identifying the occluded object, generating corresponding masks to cover it, and restoring the area. Therefore, it involves three parts: image inpainting network research, occlusion recognition and removal system, and practical application in farmland. Soil Surface Occlusion Image Inpainting Network (SOI NET) was proposed for image inpainting based on Generative Adversarial Networks (GAN). A two-stage generation network was designed: Rough Network and Refinement Network which uses the coherent semantic attention (CSA) module to improve the network's context information utilization capability. A soil surface image occlusion removal system was designed, including vehicle-mounted detection system, occlusion target recognition, mask generation and occlusion removal. Indoor and field models for estimating SMC using image color parameters were established. To verify the performance of SOI NET, experiments were carried out to compare with the traditional method and other deep learning methods. The actual farmland occlusion removal experiment was carried out with weeds as the removal object which are the most common occlusion. The results revealed that the system can effectively remove the occlusion from image surface images. In order to verify the improvement effect of occlusion removal image on the detection accuracy of vehicle-mounted terminals, further farmland experiments were conducted to estimate SMC. R2 increased from 0.637 to 0.667 and Root Mean Square Error (RMSE) decreased from 1.916 % to 1.822 % after occlusion removal. SOI NET and soil occlusion removal system can make the processed images reflect the real soil conditions and provide help for soil image analysis. [ABSTRACT FROM AUTHOR]

• A vehicle mounted soil bulk density detection system was designed and developed. • The entire process of measuring a single sampling point takes less than 10 s. • Multivariate data fusion can accurately estimate soil bulk density. • Soil mechanical resistance contributes the most to the estimation of soil bulk density. • The system can also evaluate the degree of soil compactness. Soil bulk density (BD) reflects the soil's ability to function for structural support, water and solute movement, and soil aeration. It is used to express soil physical and biological measurements on a volumetric basis for soil quality assessment and comparisons between management systems in precision agriculture. In order to solve the problem that it is difficult to directly obtain the BD with large sample size, this study introduces a self-developed vehicle-mounted BD detection system, which is a composite sensor. Through the collection and analysis of soil electrical conductivity (ECa), soil mechanical resistance (MR) and soil surface images (SIMG), BD is estimated. A systematic verification test was carried out in the experimental field in North China, the study depth was 0.2 m. The R2 and RMSE between the estimated BD and actual BD measured by core sampling were 0.647 and 0.173 (using Multiple Linear Regression), 0.591 and 0.160 (using Ridge Regression), 0.719 and 0.090 (using Gradient Boosting Decision Tree). Soil moisture content (SMC) was included into the model of BD and R2 of the model was increased from 0.719 to 0.775, RMSE was reduced from 0.090 to 0.073. The system can estimate the degree of soil compactness with R2 = 0.708 and RMSE = 0.049. SMC can also be estimated from ECa and MR (R2 = 0.481, RMSE = 1.658). These results show the potential of the vehicle-mounted BD detection system, which can provide the basis for fast, economical, and efficient soil physical property analysis. [ABSTRACT FROM AUTHOR]