As a new non-destructive testing technology, near-infrared spectroscopy has broad application prospects in agriculture, food, and other fields. In this paper, an intelligent near-infrared diffuse reflectance spectroscopy scheme (INIS) for the non-destructive testing of the sugar contents in vegetables and fruits was proposed. The cherry tomato were taken as the research object. The applicable objects and features of the three main methods of near-infrared detection were compared. According to the advantages and disadvantages of the three near infrared (NIR) detection methods, the experiment was carried out. This experiment involved the near-infrared diffuse reflection detection method, and the back propagation (BP) network model was established to research the sugar content of the cherry tomatoes. We used smoothing and a principal component analysis (PCA) to extract the final spectrum from the experimental spectrum. Taking the preprocessed spectral data as the input of the network and the measured sugar content of the cherry tomatoes as the output, the 80-12-1 network model structure was established. The cross-validation coefficient of determination was 0.8328 and the mean absolute deviation was 0.5711. The results indicate that the BP neural network can quickly and effectively detect the sugar content in cherry tomatoes. This intelligent near-infrared diffuse reflectance spectroscopy (INIS) scheme can be extended and optimized for almost all sugar-containing fruits and vegetables in the future.

Traditional soil nitrogen detection methods have the characteristics of being time-consuming and having an environmental pollution effect. We urgently need a rapid, easy-to-operate, and non-polluting soil nitrogen detection technology. In order to quickly measure the nitrogen content in soil, a new method for detecting the nitrogen content in soil is presented by using a near-infrared spectrum technique and random forest regression (RF). Firstly, the experiment took the soil by the Xunsi River in the area of Hubei University of Technology as the research object, and a total of 143 soil samples were collected. Secondly, NIR spectral data from 143 soil samples were acquired, and chemical and physical methods were used to determine the content of nitrogen in the soil. Thirdly, the raw spectral data of soil samples were denoised by preprocessing. Finally, a forecast model for the soil nitrogen content was developed by using the measured values of components and modeling algorithms. The model was optimized by adjusting the changes in the model parameters and Gini coefficient (∆Gini), and the model was compared with the back propagation (BP) and support vector machine (SVM) models. The results show that: the RF model modeling set prediction R2C is 0.921, the RMSEC is 0.115, the test set R2P is 0.83, and the RMSEP is 0.141; the detection of the soil nitrogen content can be realized by using a near-infrared spectrum technique and random forest algorithm, and its prediction accuracy is better than that of the BP and SVM models; using ∆ Gini to optimize the RF modeling data, the spectral information of the soil nitrogen content can be extracted, and the data redundancy can be reduced effectively.

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The internal humidity is very important in Proton Exchange Membrane Fuel Cell, but it cannot be measured directly because of the closed structure of the cell. It has been proofed that humidity has strongly contract with some internal parameters such as resistance, temperature and gas pressure. After studying and analysis on classical humidity mechanism models, this paper operated some experiments on PEMFC runtime humidity based on soft sensing technology, then established and test humidity soft sensing model based on runtime internal resistance. The experiment results shown that the output value of soft sensing model matched well with the theoretical calculation data. And the errors were almost within 1%. It verified the effectiveness of soft sensing model based on internal resistance.

Abstract. Images from consumer-grade cameras typically contain significant radial distortions, resulting in less accurate photogrammetry results generated using Structure from Motion (SfM). It is prone to reconstruct inaccurate scene points, becoming the so-called doming effect. The main reason is that in bundle adjustment, the last step of SfM, radial distortion is insufficiently estimated even though self-calibrating bundle adjustment was carried out. This paper designed a criterion to measure the radial distortion before SfM calculation, called Criterion on Radial Distortion (CRD). Firstly, the feature points were extracted and matched on the image; then, the image was divided into circular blocks, and the fundamental matrices were estimated by the matching points in different ring sub-blocks; finally, the difference between these fundamental matrices was used to reflect the radial distortion. Experiments on simulation data show that CRD can accurately reflect the radial distortion, which verifies its validity. Using actual UAV image data to conduct SfM experiments, CRD is consistent with the self-calibration results of Agisoft Metashape. After evaluating the accuracy of the elevation of scene points, it verifies its necessity for SfM.