Rice is one of the most important food crops in the world, and rice seed varieties are related to the yield and quality of rice. This study used near-infrared (NIR) hyperspectral technology with conventional machine learning methods (support vector machine (SVM), logistic regression (LR), and random forest (RF)) and deep learning methods (LeNet, GoogLeNet, and residual network (ResNet)) to establish variety identification models for five common types of rice seeds. Among the deep learning methods, the classification accuracies of most models were higher than 95%. This study further used the deep learning methods to establish variety identification models for 10 varieties of rice seeds without considering their types. Among them, the ResNet model had the best classification results. The classification accuracy on the test set was 86.08%. This study used the saliency map method to visualize each convolutional neural network (CNN) model to find the band region that contributed the most to the data. The results showed that the bands with the largest data contribution were mainly concentrated at approximately 1300−1400 nm and secondarily concentrated at approximately 1050−1250 nm. The overall results showed that NIR hyperspectral imaging technology combined with deep learning could effectively distinguish rice seeds of different varieties. This method provided an effective way to identify rice seed varieties in a quick and nondestructive manner.

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Tribute Citru is a natural citrus hybrid with plenty of vitamins and nutrients. Fruits’ soluble solids content (SSC) is a critical quality index. This study used hyperspectral imaging at two spectral ranges (400–1000 nm and 900–1700 nm) to determine SSC in Tribute Citru. Partial least squares regression (PLSR) and support vector regression (SVR) models were established in order to determine SSC using the spectral information of the calyx and blossom ends. The average spectra of both ends as well as their fusion was studied. The successive projections algorithm (SPA) and the correlation coefficient analysis (CCA) were used to examine the differences in characteristic wavelengths between the two ends. Most models achieved performances with the correlation coefficient of the training, validation, and testing sets over 0.6. Results showed that differences in the performances among the models using the one-sided and two-sided spectral information. No particular regulation could be found for the differences in model performances and characteristic wavelengths. The results illustrated that the sampling side was an influencing factor but not the determinant factor for SSC determination. These results would help with the development of real-world applications for citrus quality inspection without concerning the sampling sides and the spectral ranges.

The traditional detection method of CO2 concentration in seed respiration has defects such as low detection accuracy, low detection efficiency, and inability to monitor in real time. In order to solve these problems, we report a seed respiration CO2 detection system based on wavelength modulation spectroscopy (WMS) techniques in tunable diode laser absorption spectroscopy (TDLAS). This system uses a 2004 nm distributed feedback (DFB) laser as the light source, and a double-layer seed respiration device (about 1.5 L) is designed based on Herriott cell with an effective optical path of about 21 meters. Then, the second harmonic (2f) signal is extracted by the wavelength modulation method for CO2 concentration inversion. When the ambient temperature and pressure changes greatly, the corrected 2f signal is used for CO2 concentration inversion to improve the accuracy. A series of verification and comparison experiments have proved that the seed respiration CO2 detection system has the advantages of strong stability, high sampling frequency, and high detection accuracy. Finally, we used the developed system to measure the respiration intensity and respiration rate of 1 g corn seeds. The respiration intensity curves and respiration rate change details show that the seed respiration CO2 detection system is more suitable for a small amount of seeds than nondispersive infrared (NDIR) CO2 sensor and gas chromatography in real-time monitoring of the breathing process.

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