Soil nitrogen content is one of the important growth nutrient parameters of crops. It is a prerequisite for scientific fertilization to accurately grasp soil nutrient information in precision agriculture. The information about nutrients such as nitrogen in the soil can be obtained quickly by using a near-infrared sensor. The data can be analyzed in the detection process, which is nondestructive and non-polluting. In order to investigate the effect of soil pretreatment on nitrogen content by near infrared sensor, 16 nitrogen concentrations were mixed with soil and the soil samples were divided into three groups with different pretreatment. The first group of soil samples with strict pretreatment were dried, ground, sieved and pressed. The second group of soil samples were dried and ground. The third group of soil samples were simply dried. Three linear different modeling methods are used to analyze the spectrum, including partial least squares (PLS), uninformative variable elimination (UVE), competitive adaptive reweighted algorithm (CARS). The model of nonlinear partial least squares which supports vector machine (LS-SVM) is also used to analyze the soil reflectance spectrum. The results show that the soil samples with strict pretreatment have the best accuracy in predicting nitrogen content by near-infrared sensor, and the pretreatment method is suitable for practical application.

Rice grain moisture has a great impact on th production and storage storage quality of rice. The main objective of this study was to design and develop a rapid-detection sensor for rice grain moisture based on the Near-infrared spectroscopy (NIR) characteristic band, aiming to realize its accurate and on-line measurement. In this paper, the NIR spectral information of grain samples with different moisture content was obtained using a portable NIR spectrometer. Then, the partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were applied to model and analyze the spectral data to find the rice grain moisture NIR spectroscopy. As a result, the 1450 nm band was sensitive to the rice grain moisture and a rapid-detection sensor was developed with a 1450 nm light emitting diode (LED) light source, InGaAs photodiode, lens and filter, whose basic principle is to establish the relationship between the rice grain moisture and the measured voltage signal. To evaluate the sensor performance, rice grain samples with 13–30% moisture content were detected, the coefficient of determination R2 was 0.936, and the sum of squares for error (SSE) was 23.44. It is concluded that this study provides a spectroscopic measuring method, as well as developing an effective and accurate sensor for the rapid determination of rice grain moisture, which is of great significance for monitoring the quality of rice grain during its production, transportation and storage process.

The terahertz (THz) spectra of rapeseed leaves with different water content (WC) were investigated. The transmission and absorption spectra in the range of 0.3–2 THz were measured by using THz time-domain spectroscopy. The mean transmittance and absorption coefficients were applied to analyze the change regulation of WC. In addition, the Savitzky-Golay method was performed to preprocess the spectra. Then, the partial least squares (PLS), kernel PLS (KPLS), and Boosting-PLS were conducted to establish models for predicting WC based on the processed transmission and absorption spectra. Reliable results were obtained by these three methods. KPLS generated the best prediction accuracy of WC. The prediction coefficient correlation (Rval) and root mean square error (RMSEP) of KPLS based on transmission were Rval = 0.8508, RMSEP = 0.1015, and that based on absorption were Rval = 0.8574, RMSEP = 0.1009. Results demonstrated that THz spectroscopy combined with modeling methods provided an efficient and feasible technique for detecting plant physiological information.

Deltamethrin is widely used in pest prevention and control such as red spiders, aphids, and grubs in strawberry. It is important to accurately monitor whether the deltamethrin residue in strawberry exceeds the standard. In this paper, density functional theory (DFT) was used to theoretically analyze the molecular structure of deltamethrin, gold nanoparticles (AuNPs) and silver nanoparticles (AgNPs) were used to enhance the surface enhanced Raman spectroscopy (SERS) detection signal. As a result, the theoretical Raman peaks of deltamethrin calculated by DFT were basically similar to the measured results, and the enhancing effects based on AuNPs was better than that of AgNPs. Moreover, 554, 736, 776, 964, 1000, 1166, 1206, 1593, 1613, and 1735 cm−1 could be determined as deltamethrin characteristic peaks, among which only three Raman peaks (736, 1000, and 1166 cm−1) could be used as the deltamethrin characteristic peaks in strawberry when the detection limit reached 0.1 mg/L. In addition, the 500–1800 cm−1 SERS of deltamethrin were analyzed by the partial least squares (PLS) and backward interval partial least squares (BIPLS). The prediction accuracy of deltamethrin in strawberry (Rp2 = 0.93, RMSEp = 4.66 mg/L, RPD = 3.59) was the highest when the original spectra were pretreated by multiplicative scatter correction (MSC) and then modeled by BIPLS. In conclusion, the deltamethrin in strawberry could be qualitatively analyzed and quantitatively determined by SERS based on AuNPs enhancement, which provides a new detection scheme for deltamethrin residue determination in strawberry.