The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000–2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.

Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.

The traditional method used to determine the moisture content of tea leaves is time consuming and destructive. To address this problem, an effective and non-destructive prediction method based on near-infrared spectroscopy (NIRS) is proposed in this paper. This new method combines discrete wavelet transforms (DWT) with the bootstrap soft shrinkage algorithm (BOSS). To eliminate uninformative or interfering variables, DWT is applied to remove the noise in the spectral data by decomposing the origin spectrum into six layers. BOSS is used to select informative variables by reducing the dimensions of the sub-layers’ reconstruction spectrum. After selecting the effective variables using DWT and BOSS, a prediction model based on partial least squares (PLS) is built. To validate effectiveness and stability of the prediction model, full-spectrum PLS, genetic algorithm PLS (GA-PLS), and interval PLS (iPLS) were compared with the proposed method. The experiment results illustrate that the proposed prediction model outperforms the other classical models considered in this study and shows promise for the prediction of the moisture content in Yinghong No. 9 tea leaves.

Visible/near-infrared (VIS/NIR) spectroscopy is a powerful tool for rapid, nondestructive fruit quality detection. This technology has been widely applied for quality detection of small, thin-peeled fruit, though less so for large, thick-peeled fruit due to a weak spectral signal resulting in a reduction of accuracy. More modeling work should be focused on solving this problem. “Shatian” pomelo is a traditional Chinese large, thick-peeled fruit, and granulation and water loss are two major internal quality factors that influence its storage quality. However, there is no efficient, nondestructive detection method for measuring these factors. Thus, the VIS/NIR spectral signal detection of 120 pomelo samples during storage was performed. Information mining (singular sample elimination, data processing, feature extraction) and modeling were performed in different ways to construct the optimal method for achieving an accurate detection. Our results showed that the water content of postharvest pomelo was optimally detected using the Savitzky–Golay method (SG) plus the multiplicative scatter correction method (MSC) for data processing, genetic algorithm (GA) for feature extraction, and partial least squares regression (PLSR) for modeling (the coefficient of determination and root mean squared error of the validation set were 0.712 and 0.0488, respectively). Granulation degree was best detected using SG for data processing and PLSR for modeling (the detection accuracy of the validation set was 100%). Additionally, our research showed a weak relationship between the pomelo water content and granulation degree, which provided a reference for the existing debates. Therefore, our results demonstrated that VIS/NIR combined with optimal information mining and modeling methodswas feasible for determining the water content and granulation degree of postharvest pomelo, and for providing references for the nondestructive internal quality detection of other large, thick-peeled fruits.

Recent studies have indicated that spray-dried porcine plasma (SDPP) is a potential transmission route for African swine fever (ASF). Therefore, it is essential to develop rapid, high-efficiency analytical methods to detect SDPP, aiming to both restrict the abuse of SDPP and block the spread of ASF through feed additive. The feasibility of detecting SDPP using an electronic nose and near-infrared spectroscopy (NIRS) is explored and validated by a principal component analysis (PCA). Both discrimination experiments and prediction experiments were implemented to compare the detect feature of the two techniques. On this basis, partial least squares discriminant analysis (PLS–DA) under various preprocessing methods was used to develop a qualitative discriminant model for estimating the prediction performance. Before selecting a specific regression model for the quantitative analysis of SDPP, a continuum regression (CR) model was employed to explore and choose the potential most appropriate regression model for these two different types of datasets. The results showed that the optimal regression model adopted partial least squares regression (PLSR) with the Savitzky–Golay first derivative and mean-center preprocessing for the NIRS dataset ( R p 2 = 0.999, RMSEP = 0.1905). Overall, combining the NIRS technique with multivariate data analysis methods shows more possibilities than an electronic nose for rapidly detecting the usage of SDPP in mixed feed samples, which could provide an effective way to control the spread of ASF.

The autonomous navigation of unmanned vehicles in GPS denied environments is an incredibly challenging task. Because cameras are low in price, obtain rich information, and passively sense the environment, vision based simultaneous localization and mapping (VSLAM) has great potential to solve this problem. In this paper, we propose a novel VSLAM framework based on a stereo camera. The proposed approach combines the direct and indirect method for the real-time localization of an autonomous forklift in a non-structured warehouse. Our proposed hybrid method uses photometric errors to perform image alignment for data association and pose estimation, extracts features from keyframes, and matches them to acquire the updated pose. By combining the efficiency of the direct method and the high accuracy of the indirect method, the approach achieves higher speed with comparable accuracy to a state-of-the-art method. Furthermore, the two step dynamic threshold feature extraction method significantly reduces the operating time. In addition, a motion model of the forklift is proposed to provide a more reasonable initial pose for direct image alignment based on photometric errors. The proposed algorithm is experimentally tested on a dataset constructed from a large scale warehouse with dynamic lighting and long corridors, and the results show that it can still successfully perform with high accuracy. Additionally, our method can operate in real time using limited computing resources.

Visible/near-infrared (VIS/NIR) spectroscopy is a powerful tool for rapid, nondestructive fruit quality detection. This technology has been widely applied for quality detection of small thin-peel fruit, although less so for large thick-peel fruit because of the low signal-to-noise ratio of the spectral signal, resulting in a reduction of accuracy. More modeling work should be focused on solving this problem. This research explored a method of spectroscopy for the total soluble solid (TSS) content and acidity detection of ‘Shatian’ pomelo, which are two major parameters of fruit internal flavor. VIS/NIR spectral signal detection of 100 pomelo samples during storage was performed. Detection based on raw data, signal jitter, and scattered light noise removal, feature extraction, and deep learning were performed and combined with modeling detection to achieve an accurate step-by-step detection. Our results showed that 600 W is the optimal light intensity for detecting the internal flavor of pomelo. The TSS content of pomelo is optimally detected using Savitzky-Golay (SG) + multiplicative scatter correction (MSC) + genetic algorithm (GA) + principal component analysis (PCA) + convolutional neural network (CNN) + partial least squares regression (PLSR); however, acidity of pomelo is optimally detected using SG + MSC + GA + PLSR. With the optimal detection method, the coefficient of determination and root mean squared error (RMSE) of the validation set for TSS detection are 0.72 and 0.49, respectively; and for acidity detection are 0.55 and 0.10, respectively. Even though the accuracy is not high, the data are still acceptable and helpful in nondestructive quality grading of large quantities postharvest fruit. Therefore, our results demonstrated that VIS/NIR was feasible for detecting the TSS content and acidity of postharvest pomelo, and for providing a possible method for the nondestructive internal quality detection of other large thick-peel fruit.

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