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The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of “Luogang” orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky–Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of “Luogang” orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits.
Sai Xu; Huazhong Lu; Christopher Ference; Qianqian Zhang. An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange. Electronics 2021, 10, 80 .
AMA StyleSai Xu, Huazhong Lu, Christopher Ference, Qianqian Zhang. An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange. Electronics. 2021; 10 (1):80.
Chicago/Turabian StyleSai Xu; Huazhong Lu; Christopher Ference; Qianqian Zhang. 2021. "An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in “Luogang” Orange." Electronics 10, no. 1: 80.
We, the authors, wish to make the following corrections to our paper
Xiaoteng Han; Enli Lü; Huazhong Lu; Fanguo Zeng; Guangjun Qiu; Qiaodong Yu; Min Zhang. Correction: Han et al. Detection of Spray-Dried Porcine Plasma (SDPP) Based on Electronic Nose and Near-Infrared Spectroscopy Data. Appl. Sci. 2020, 10, 2967. Applied Sciences 2020, 10, 6433 .
AMA StyleXiaoteng Han, Enli Lü, Huazhong Lu, Fanguo Zeng, Guangjun Qiu, Qiaodong Yu, Min Zhang. Correction: Han et al. Detection of Spray-Dried Porcine Plasma (SDPP) Based on Electronic Nose and Near-Infrared Spectroscopy Data. Appl. Sci. 2020, 10, 2967. Applied Sciences. 2020; 10 (18):6433.
Chicago/Turabian StyleXiaoteng Han; Enli Lü; Huazhong Lu; Fanguo Zeng; Guangjun Qiu; Qiaodong Yu; Min Zhang. 2020. "Correction: Han et al. Detection of Spray-Dried Porcine Plasma (SDPP) Based on Electronic Nose and Near-Infrared Spectroscopy Data. Appl. Sci. 2020, 10, 2967." Applied Sciences 10, no. 18: 6433.
Cold-storage containers are widely used in cold-chain logistics transportation due to their energy saving, environmental protection, and low operating cost. The uniformity of temperature distribution is significant in agricultural-product storage and transportation. This paper explored temperature distribution in the container by numerical simulation, which included ventilation velocity and the fan location. Numerical model/numerical simulation showed good agreement with experimental data in terms of temporal and spatial air temperature distribution. Results showed that the cooling rate improved as velocity increased, and temperature at 45 min was the lowest, when velocity was 16 m/s. Temperature-distribution uniformity in the compartment became worse with the increase in ventilation velocity, but its lowest temperature decreased with a velocity increase. With regard to fan energy consumption, the cooling rate of the cooling module, and temperature-field distribution in the product area, velocity of 12 m/s was best. Temperature standard deviation and nonuniformity coefficient in the container were 0.87 and 2.1, respectively, when fans were located in the top four corners of the container. Compared with before, the average temperature in the box was decreased by 0.12 °C, and the inhomogeneity coefficient decreased by more than twofold. The results of this paper provide a better understanding of temperature distribution in cold-storage containers, which helps to optimize their structure and parameters.
Bin Li; Jiaming Guo; Jingjing Xia; Xinyu Wei; Hao Shen; Yongfeng Cao; Huazhong Lu; Enli Lü. Temperature Distribution in Insulated Temperature-Controlled Container by Numerical Simulation. Energies 2020, 13, 4765 .
AMA StyleBin Li, Jiaming Guo, Jingjing Xia, Xinyu Wei, Hao Shen, Yongfeng Cao, Huazhong Lu, Enli Lü. Temperature Distribution in Insulated Temperature-Controlled Container by Numerical Simulation. Energies. 2020; 13 (18):4765.
Chicago/Turabian StyleBin Li; Jiaming Guo; Jingjing Xia; Xinyu Wei; Hao Shen; Yongfeng Cao; Huazhong Lu; Enli Lü. 2020. "Temperature Distribution in Insulated Temperature-Controlled Container by Numerical Simulation." Energies 13, no. 18: 4765.
Granulation is a physiological disorder of juice sacs in citrus fruit, causing juice sacs to become hard and dry and resulting in decreased internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is difficult to identify the extent of granulation by observation of the outer peel and fruit shape. In this study, a rapid and non-destructive testing method using visible and near-infrared transmittance spectroscopy combined with machine vision technology was applied to identify and estimate granulation inside fruit. A total of 600 samples in different growth periods was harvested, and fruit were divided into five classes according to five granulation levels. Spectral data were obtained for two ranges of 400–1100 nm and 900–1700 nm by visible and near-infrared transmittance spectroscopy. In addition, chemometrics were used to measure the chemical changes of soluble solid content (SSC), titratable acidity (TA), and moisture content (MC) caused by different granulation levels. Machine vision technology can rapidly estimate the external characteristics of samples and measure the physical changes in mass and volume caused by different granulation levels. Compared with using a single or traditional methods, the predictive performances of multi-category classification models (PCA-SVM and PCA-GRNN) (Table A1) were significantly enhanced. In particular, the model accuracy rate (ARM) was 99% for PCA-GRNN, with classification accuracy (CA), classification sensitivity (CS), and classification specificity (CSP) of 0.9950, 0.9750, and 0.9934, respectively. The results showed that this method has great potential for the identification and estimation of granulation. Multi-source data fusion and application of a multi-category classification model with the smallest number of input layers and acceptable high predictive performances are proposed for on-line applications. This method can be effectively used on-line for the non-destructive detection of fruits with granulation.
Xiaopeng Sun; Sai Xu; Huazhong Lu. Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology. Applied Sciences 2020, 10, 5399 .
AMA StyleXiaopeng Sun, Sai Xu, Huazhong Lu. Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology. Applied Sciences. 2020; 10 (16):5399.
Chicago/Turabian StyleXiaopeng Sun; Sai Xu; Huazhong Lu. 2020. "Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology." Applied Sciences 10, no. 16: 5399.
During air-assisted spraying operations in orchards, the interaction between the droplets and the target leaves has a decisive influence on the retention of the droplets on the leaves and the final deposition state. Based on the observation of the final deposition effect of the droplets in the spray test, the retention state of the droplets on the leaves is divided into three categories: uniform distribution (hereinafter referred to as uniform), accumulation, and loss. During the initial interaction between the droplets and the leaves, the adhesion or sliding state of the droplets has an important influence on the final deposition state of the droplets, which is determined by the target leaf adhesion work in this paper. Based on obtaining the characteristic parameters of the leaf surface, a theoretical model of adhesion work related to parameters such as the contact angle, rough factor, and initial tilt angle of the leaf is established. Afterward, through the connection of the droplet coverage on the macro level, the establishment of the deposition state model of the droplet group on the leaf is completed. By conducting the experiment test based on the Box-Behnken design of response surface methodology (RSM), the droplet deposition states under the influence of the spray distance, fan outlet wind speed and droplet size were studied and compared with the predicted values. The test results show that the prediction accuracies of the three states of uniform, accumulation, and loss were 87.5%, 80%, and 100%, respectively. The results of the study indicate that the established prediction model can effectively predict the deposition states of droplets on leaves and provide a reference for the selection of spray operation parameters.
Jun Li; Huajun Cui; Yakun Ma; Lu Xun; Zhiqiang Li; Zhou Yang; Huazhong Lu. Orchard Spray Study: A Prediction Model of Droplet Deposition States on Leaf Surfaces. Agronomy 2020, 10, 747 .
AMA StyleJun Li, Huajun Cui, Yakun Ma, Lu Xun, Zhiqiang Li, Zhou Yang, Huazhong Lu. Orchard Spray Study: A Prediction Model of Droplet Deposition States on Leaf Surfaces. Agronomy. 2020; 10 (5):747.
Chicago/Turabian StyleJun Li; Huajun Cui; Yakun Ma; Lu Xun; Zhiqiang Li; Zhou Yang; Huazhong Lu. 2020. "Orchard Spray Study: A Prediction Model of Droplet Deposition States on Leaf Surfaces." Agronomy 10, no. 5: 747.
Since the first proposal to use spray-dried porcine plasma (SDPP) as an animal-based protein source feed additive for piglets in the late 1980s, a large number of studies have been published on the promotion effect of SDPP on piglets. SDPP contains biologically active components that support pig health during weaning stress and may be more economical to use compared to similar bovine-milk-derived protein sources. Unfortunately, animal blood proteins have been suspected as a source for African Swine Fever Virus (ASFV) spread in China. Furthermore, there are no offcially recognized methods for quantifying SDPP in complex feed mixtures. Therefore, it is essential to develop rapid, high-effciency analytical methods to detect SDPP. The feasibility of detecting SDPP using an electronic nose and near-infrared spectroscopy (NIRS) was 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 (Rp2 = 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 identify the use of SDPP in feed mixtures.
Xiaoteng Han; Enli Lü; Huazhong Lu; Fanguo Zeng; Guangjun Qiu; Qiaodong Yu; Min Zhang. Detection of Spray-Dried Porcine Plasma (SDPP) based on Electronic Nose and Near-Infrared Spectroscopy Data. Applied Sciences 2020, 10, 2967 .
AMA StyleXiaoteng Han, Enli Lü, Huazhong Lu, Fanguo Zeng, Guangjun Qiu, Qiaodong Yu, Min Zhang. Detection of Spray-Dried Porcine Plasma (SDPP) based on Electronic Nose and Near-Infrared Spectroscopy Data. Applied Sciences. 2020; 10 (8):2967.
Chicago/Turabian StyleXiaoteng Han; Enli Lü; Huazhong Lu; Fanguo Zeng; Guangjun Qiu; Qiaodong Yu; Min Zhang. 2020. "Detection of Spray-Dried Porcine Plasma (SDPP) based on Electronic Nose and Near-Infrared Spectroscopy Data." Applied Sciences 10, no. 8: 2967.
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.
Sai Xu; Huazhong Lu; Christopher Ference; Guangjun Qiu; Xin Liang. Rapid Nondestructive Detection of Water Content and Granulation in Postharvest “Shatian” Pomelo Using Visible/Near-Infrared Spectroscopy. Biosensors 2020, 10, 41 .
AMA StyleSai Xu, Huazhong Lu, Christopher Ference, Guangjun Qiu, Xin Liang. Rapid Nondestructive Detection of Water Content and Granulation in Postharvest “Shatian” Pomelo Using Visible/Near-Infrared Spectroscopy. Biosensors. 2020; 10 (4):41.
Chicago/Turabian StyleSai Xu; Huazhong Lu; Christopher Ference; Guangjun Qiu; Xin Liang. 2020. "Rapid Nondestructive Detection of Water Content and Granulation in Postharvest “Shatian” Pomelo Using Visible/Near-Infrared Spectroscopy." Biosensors 10, no. 4: 41.
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.
Feiren Wang; Enli Lü; Yu Wang; Guangjun Qiu; Huazhong Lu. Efficient Stereo Visual Simultaneous Localization and Mapping for an Autonomous Unmanned Forklift in an Unstructured Warehouse. Applied Sciences 2020, 10, 698 .
AMA StyleFeiren Wang, Enli Lü, Yu Wang, Guangjun Qiu, Huazhong Lu. Efficient Stereo Visual Simultaneous Localization and Mapping for an Autonomous Unmanned Forklift in an Unstructured Warehouse. Applied Sciences. 2020; 10 (2):698.
Chicago/Turabian StyleFeiren Wang; Enli Lü; Yu Wang; Guangjun Qiu; Huazhong Lu. 2020. "Efficient Stereo Visual Simultaneous Localization and Mapping for an Autonomous Unmanned Forklift in an Unstructured Warehouse." Applied Sciences 10, no. 2: 698.
The maturity of seeds at harvest determines their intrinsic quality characteristics such as longevity and vigor, and these characteristics are dominant factors for seed quality evaluation in the seed industry. However, little information is available on how to identify and further classify the maturation stage of seeds in a way that is nondestructive, precise, rapid, and inexpensive, while also exactly meeting the need for the uniform control of seed performance in the seed industry to improve crop yield. This study demonstrated a nondestructive method for detecting seed maturity by using the single-kernel near-infrared spectroscopy (SK-NIRS) technique. The results showed that five classes of cucumber seeds with different maturation levels can be distinguished successfully. A tree-structured hierarchical classification strategy consisting of one soft independent modeling of class analogy (SIMCA) model and three partial least squares discriminant analysis (PLS-DA) models were proposed ending up with 99.69% of the overall classification accuracy and 0.9961 of Cohen’s kappa in the test set, and its predictive performance was superior to both SIMCA and PLS-DA for direct multiclass classification. SK-NIRS in combination with a multiclass hierarchical classification strategy was proved to be both intuitive and efficient in classifying cucumber seeds according to maturation levels.
Fanguo Zeng; Enli Lü; Guangjun Qiu; Huazhong Lu; Biao Jiang; Lu; Zeng; Qiu. Single-Kernel FT-NIR Spectroscopy for Detecting Maturity of Cucumber Seeds Using a Multiclass Hierarchical Classification Strategy. Applied Sciences 2019, 9, 5058 .
AMA StyleFanguo Zeng, Enli Lü, Guangjun Qiu, Huazhong Lu, Biao Jiang, Lu, Zeng, Qiu. Single-Kernel FT-NIR Spectroscopy for Detecting Maturity of Cucumber Seeds Using a Multiclass Hierarchical Classification Strategy. Applied Sciences. 2019; 9 (23):5058.
Chicago/Turabian StyleFanguo Zeng; Enli Lü; Guangjun Qiu; Huazhong Lu; Biao Jiang; Lu; Zeng; Qiu. 2019. "Single-Kernel FT-NIR Spectroscopy for Detecting Maturity of Cucumber Seeds Using a Multiclass Hierarchical Classification Strategy." Applied Sciences 9, no. 23: 5058.
The visible/near infrared (VIS/NIR) spectrometer and electronic nose (E-nose) are two commonly used portable and nondestructive detection apparatuses which have a promising application for the quick acquisition of fruit’s internal quality in both the orchard and market. However, the accuracy of these instruments is sometimes unsatisfactory, especially for thick peeled fruit like the ‘Aiyuan 38’ orange, which was investigated in this research. The objective of this research was to find a method to improve the accuracy for the detection of an orange’s total soluble solid content (TSS) using a VIS/NIR spectrometer and E-nose. Different spectrum detection positions and conventional feature extraction methods are compared to get the optimal data fusion parameters. The detection model was then built up based on the obtained fusion data under the optimal parameters. Partial least squares regression (PLSR) and mutual information theory (MIT) were applied for feature extraction, and PLSR and principal component analysis (PCA)-back propagation neural network (BPNN) were applied for modeling and detection. PLSR results showed that the sampling reflection spectrum at the position of the calyx results in a better orange TSS detection than other sampling positions. For VIS/NIR reflection spectrum feature extraction, PLSR and MIT results showed that the optimal data process + feature extraction method is Savitzky-Golay + 763 features, when their mutual information values between the feature and TSS value were larger than 0.74. For E-nose feature extraction, PLSR and MIT results showed that the combined feature (combination of 75 s value, average value, average of differential value, integral value, and maximum value) is the optimal feature extraction method, and all features are retained for modeling. The PLSR detection ability of orange TSS based on fusion data is better than the single detection method, with the detection ability of the single detection methods being unsatisfactory. PCA-BPNN has better orange TSS detection ability than PLSR. The R2, RMSE, and slope from the calibration set for PCA-BPNN detection were 0.9695, 0.1895, and 0.9665, respectively, and from the validation set for PCA-BPNN detection were 0.8872, 0.4709, and 1.0871, respectively, indicating that this method can detect orange TSS efficiently.
Sai Xu; Huazhong Lu; Christopher Ference; Qianqian Zhang. Visible/near Infrared Reflection Spectrometer and Electronic Nose Data Fusion as an Accuracy Improvement Method for Portable Total Soluble Solid Content Detection of Orange. Applied Sciences 2019, 9, 3761 .
AMA StyleSai Xu, Huazhong Lu, Christopher Ference, Qianqian Zhang. Visible/near Infrared Reflection Spectrometer and Electronic Nose Data Fusion as an Accuracy Improvement Method for Portable Total Soluble Solid Content Detection of Orange. Applied Sciences. 2019; 9 (18):3761.
Chicago/Turabian StyleSai Xu; Huazhong Lu; Christopher Ference; Qianqian Zhang. 2019. "Visible/near Infrared Reflection Spectrometer and Electronic Nose Data Fusion as an Accuracy Improvement Method for Portable Total Soluble Solid Content Detection of Orange." Applied Sciences 9, no. 18: 3761.
The objective of this study was to find an intelligent and fast method to detect the type, blended ratio, and mixed ratio of ancient Pu'er tea, which is significant in maintaining order in the Pu'er tea industry. An electronic nose (E-nose) and a visible near infrared spectrometer (VIS/NIR spectrometer) were applied for tea sampling. Feature extraction was conducted using both the traditional method and a convolutional neural network (CNN) technique. Linear discriminant analysis (LDA) and partial least square regression (PLSR) were applied for pattern recognition. After sampling while using the traditional method, the analysis of variance (ANOVA) results showed that the mean differential value of each sensor should be selected as the optimal feature extraction method for E-nose data, and raw data comparison results showed that 19 peak/valley values and two slope values were extracted. While the format of E-nose data was in accord with the input format for CNN, the VIS/NIR spectrometer data required matrixing to meet the format requirements. The LDA and PLSR analysis results showed that CNN has superior detection ability, being able to acquire more local features than the traditional method, but it has the risk of mixing in redundant information, which can act to reduce the detection ability. Multi-source information fusion (E-nose and VIS/NIR spectrometer fusion) can collect more features from different angles to improve the detection ability, but it also contains the risk of adding redundant information, which reduces the detection ability. For practical detection, the type of Pu'er tea should be recognizable using a VIS/NIR spectrometer and the traditional feature extraction method. The blended ratio of Pu'er tea should also be identifiable by using a VIS/NIR spectrometer with traditional feature extraction. Multi-source information fusion with traditional feature extraction should be used if the accuracy requirement is extremely high; otherwise, a VIS/NIR spectrometer with traditional feature extraction is preferred.
Sai Xu; Xiuxiu Sun; Huazhong Lu; Qianqian Zhang. Detection of Type, Blended Ratio, and Mixed Ratio of Pu'er Tea by Using Electronic Nose and Visible/Near Infrared Spectrometer. Sensors 2019, 19, 2359 .
AMA StyleSai Xu, Xiuxiu Sun, Huazhong Lu, Qianqian Zhang. Detection of Type, Blended Ratio, and Mixed Ratio of Pu'er Tea by Using Electronic Nose and Visible/Near Infrared Spectrometer. Sensors. 2019; 19 (10):2359.
Chicago/Turabian StyleSai Xu; Xiuxiu Sun; Huazhong Lu; Qianqian Zhang. 2019. "Detection of Type, Blended Ratio, and Mixed Ratio of Pu'er Tea by Using Electronic Nose and Visible/Near Infrared Spectrometer." Sensors 19, no. 10: 2359.
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.
Guangjun Qiu; Enli Lü; Ning Wang; Huazhong Lu; Feiren Wang; Fanguo Zeng. Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis. Applied Sciences 2019, 9, 1530 .
AMA StyleGuangjun Qiu, Enli Lü, Ning Wang, Huazhong Lu, Feiren Wang, Fanguo Zeng. Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis. Applied Sciences. 2019; 9 (8):1530.
Chicago/Turabian StyleGuangjun Qiu; Enli Lü; Ning Wang; Huazhong Lu; Feiren Wang; Fanguo Zeng. 2019. "Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis." Applied Sciences 9, no. 8: 1530.
Wireless sensor network (WSN) bidirectional nodes, including the sensing node and the solenoid valve control node, were developed for information collection and micro-irrigation monitoring system in a litchi orchard, aimed at improving the problem of wireless communication barriers and the micro-irrigation management efficiency. The sensing node was composed of an MCU8051, a CC2530 RF chip for communication, a RFX2401for amplification. This node is supposed to collect data from a DHT22 air temperature and humidity sensor, a GY-30 light intensity sensor and a TDR-3 soil moisture sensor. The control node includes an MCU8051, a CC2530 RF chip for communication, and peripheral drive circuits for adapting the bi-stable pulse solenoid valve. The application software and backstage management software were written with these two nodes as the hardware platform, based on ZStack agreement. The maximum effective bidirectional communication distance of the designed nodes reached 1205m in unoccupied regions and 122m in litchi orchards. Within a 30 min working cycle, it could be estimated that two 3.7 V battery with a rated capacity of 3000 mA•h can power the sensing node for time up to 500d. Test results in litchi orchards show that the average packet loss rate is 0.75%. The system was processing smoothly with the above nodes for information acquisition and controlling micro-irrigation in litchi orchards.
Xie Jiaxing; Gao Peng; Wang Weixing; Lu Huazhong; Xu Xin; Hu Guosheng. Design of Wireless Sensor Network Bidirectional Nodes for Intelligent Monitoring System of Micro-irrigation in Litchi Orchards. IFAC-PapersOnLine 2018, 51, 449 -454.
AMA StyleXie Jiaxing, Gao Peng, Wang Weixing, Lu Huazhong, Xu Xin, Hu Guosheng. Design of Wireless Sensor Network Bidirectional Nodes for Intelligent Monitoring System of Micro-irrigation in Litchi Orchards. IFAC-PapersOnLine. 2018; 51 (17):449-454.
Chicago/Turabian StyleXie Jiaxing; Gao Peng; Wang Weixing; Lu Huazhong; Xu Xin; Hu Guosheng. 2018. "Design of Wireless Sensor Network Bidirectional Nodes for Intelligent Monitoring System of Micro-irrigation in Litchi Orchards." IFAC-PapersOnLine 51, no. 17: 449-454.
Mechanical damage of litchi fruits caused by impact is a major problem for mechanical harvesting. During a shaking harvesting process, the impact between fruits is almost inevitable. Thus, it is necessary to study the characteristics of fruit-to-fruit impact of litchis. Impact tests were conducted between an upper specimen and a lower specimen on an impact device with Yuhebao and Guiwei variety of litchis. The modulus of elasticity of fruits after impact was measured with a tester to determine the damage of the fruits. Three impact times and four impact speeds were used in the tests. The results show that with impact speed = 2.8 m/s, 15-times impact caused significant damage to the fruits, while 5-times may only cause slightly damage. When the impact speed decreased to 0.98 m/s, no significant damage was observed after 15-times impact for Yuhebao litchis. Therefore, increase impact times or impact speed may cause more damage to the fruits. However, when the impact times or the impact speed were lower enough, damage may be very slight or even not happen.
Weizu Wang; Zhou Yang; Huazhong Lu; Han Fu. Mechanical damage caused by fruit-to-fruit impact of litchis. IFAC-PapersOnLine 2018, 51, 532 -535.
AMA StyleWeizu Wang, Zhou Yang, Huazhong Lu, Han Fu. Mechanical damage caused by fruit-to-fruit impact of litchis. IFAC-PapersOnLine. 2018; 51 (17):532-535.
Chicago/Turabian StyleWeizu Wang; Zhou Yang; Huazhong Lu; Han Fu. 2018. "Mechanical damage caused by fruit-to-fruit impact of litchis." IFAC-PapersOnLine 51, no. 17: 532-535.
The volatiles of Brown rice plant hopper (BRPH) itself is an important evidence for BRPH electronic nose detection. However, during infestation, BRPH always sucks the juice from the rice stem, therefore, a study on the similarity between BRPH’s volatiles and undamaged rice stem volatiles might help determine whether the volatile contents of BRPH would be influenced by the sucking of the rice stem juice. If so, recognizing BRPH from rice stem should be a crucial step to reduce the misjudgment of BRPH occurrence prediction by using electronic nose, which has not been reported until now. This paper used an electronic nose (PEN3) sample of the volatile of U3IN (under the 3th-instar nymphs), O3IN (over the 3th-instar nymphs) and healthy rice stem. Hierarchical clustering analysis (HCA), Loading analysis (Loadings), principal component analysis (PCA), k-nearest neighbor (KNN), probabilistic neural network (PNN), and support vector machine (SVM) were used for data analysis. HCA, Loadings, and PCA results proved that certain similarities exist between volatiles of rice stem and BRPH, Loadings and PCA results also indicated the volatile similarity between O3IN and rice stem is stronger than the volatile similarity between U3IN and rice stem. To reduce the redundant information and improve computation efficiency, according to Loadings and PCA results, sensor R5 of electronic nose has been be removed, then, the fist four principle components has been kept as the feature values. KNN, PNN and SVM all can recognize rice stem, O3IN, and U3IN effectively, however, KNN and PNN are more fit to solve the problem of rice stem and BRHP recognition than SVM. This experiment results proved that certain similarities exist between volatiles of rice stem and BRPH, also figured out the feasible way to recognize rice stem and BRPH, which could provide a reference for further research of BRPH prediction.
Sai Xu; Zhiyan Zhou; Luhong Tian; Huazhong Lu; Xiwen Luo; Yubin Lan. Study of the similarity and recognition between volatiles of brown rice plant hoppers and rice stem based on the electronic nose. Computers and Electronics in Agriculture 2018, 152, 19 -25.
AMA StyleSai Xu, Zhiyan Zhou, Luhong Tian, Huazhong Lu, Xiwen Luo, Yubin Lan. Study of the similarity and recognition between volatiles of brown rice plant hoppers and rice stem based on the electronic nose. Computers and Electronics in Agriculture. 2018; 152 ():19-25.
Chicago/Turabian StyleSai Xu; Zhiyan Zhou; Luhong Tian; Huazhong Lu; Xiwen Luo; Yubin Lan. 2018. "Study of the similarity and recognition between volatiles of brown rice plant hoppers and rice stem based on the electronic nose." Computers and Electronics in Agriculture 152, no. : 19-25.
The objective of this study was to detect and monitor the flavor of tomatoes, as impacted by different postharvest handlings, including chilling storage (CS) and blanching treatment (BT). CS tomatoes were stored in a refrigerator at 5 °C and tested at storage day 0, 3, and 7. BT tomatoes were dipped in 50 or 100 °C water for 1 min, and tested immediately. The taste, mouth feel, and aroma of tomatoes were evaluated by testing the total soluble solid content (TSS), titratable acidity (TA), ratio of TSS and TA (TSS/TA), firmness, and electronic nose (E-nose) response to tomatoes. The experimental results showed that the CS can prevent taste and firmness loss to a certain extent, but the sensory results indicated that CS accelerated flavor loss due to the TSS/TA of CS tomatoes increasing slower than control. The taste and firmness of tomatoes were impacted slightly by 50 °C BT, and were significantly impacted by 100 °C BT. Based on physicochemical parameters, different postharvest handling treatments for tomatoes could not be classified except for the 100 °C BT treated tomatoes, which were significantly impacted in terms of taste and mouth feel. The E-nose is an efficient way to detect differences in postharvest handling treatments for tomatoes, and indicated significant aroma changes for CS and BT treated tomato fruit. The classification of tomatoes after different postharvest handling treatments, based on comprehensive flavor (physicochemical parameters and E-nose combined data), is better than that based on single physicochemical parameters or E-nose, and the comprehensive flavor of 100 °C BT tomatoes changed the most. Even so, the tomato flavor change during postharvest handlings is suggested to be detected and monitored by single E-nose data. The E-nose has also been proved as a feasible way to predict the TSS and firmness of tomato fruit rather than TA or TSS/TA, during the postharvest handing process.
Sai Xu; Xiuxiu Sun; Huazhong Lü; Hui Yang; Qingsong Ruan; Hao Huang; Minglin Chen. Detecting and Monitoring the Flavor of Tomato (Solanum lycopersicum) under the Impact of Postharvest Handlings by Physicochemical Parameters and Electronic Nose. Sensors 2018, 18, 1847 .
AMA StyleSai Xu, Xiuxiu Sun, Huazhong Lü, Hui Yang, Qingsong Ruan, Hao Huang, Minglin Chen. Detecting and Monitoring the Flavor of Tomato (Solanum lycopersicum) under the Impact of Postharvest Handlings by Physicochemical Parameters and Electronic Nose. Sensors. 2018; 18 (6):1847.
Chicago/Turabian StyleSai Xu; Xiuxiu Sun; Huazhong Lü; Hui Yang; Qingsong Ruan; Hao Huang; Minglin Chen. 2018. "Detecting and Monitoring the Flavor of Tomato (Solanum lycopersicum) under the Impact of Postharvest Handlings by Physicochemical Parameters and Electronic Nose." Sensors 18, no. 6: 1847.
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.
Guangjun Qiu; Enli Lü; Huazhong Lu; Sai Xu; Fanguo Zeng; Qin Shui. Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis. Sensors 2018, 18, 1010 .
AMA StyleGuangjun Qiu, Enli Lü, Huazhong Lu, Sai Xu, Fanguo Zeng, Qin Shui. Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis. Sensors. 2018; 18 (4):1010.
Chicago/Turabian StyleGuangjun Qiu; Enli Lü; Huazhong Lu; Sai Xu; Fanguo Zeng; Qin Shui. 2018. "Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis." Sensors 18, no. 4: 1010.
Since gas sensor drift is a main limitation for the application of an electronic nose, and a reference standard is necessary for shelf management of litchi fruit, a modified mean deviation threshold function based on fast Fourier transform (MDFF–FFT) for electronic nose drift elimination and a new concept the rest storage life (RSL) for litchi fruit shelf situation evaluation have been constructed in this study. Three commonly used threshold acquisition methods, unbiased estimator, fixed threshold, and mini-max principle were evaluated to instead of selecting threshold value randomly for present MDFF–FFT. A PEN3 portable electronic nose was applied to recognize the RSL of litchi during storage across room temperature (RT), refrigerator environment (RE) and controlled-atmosphere (CA) environments. Linear discriminant analysis (LDA), probabilistic neural network (PNN), and partial least squares regression (PLSR) were used to compare the RSL classification effect, recognition accuracy, and predict ability of litchi stored in the three environments based on electronic nose with the drift elimination of different threshold acquisition methods using MDTF–FFT. The results showed that an electronic nose has the potential to recognize the RSL of litchi stored in different environments. Unbiased estimator method can provide better threshold than other threshold acquisition methods for MDTF–FFT. After drift elimination by unbiased estimator method combined with MDTF–FFT, litchi RSL can be classified, recognized and predicted by electronic nose effectively, the accuracy of which was higher than control (no drift elimination) and drift elimination with other methods.
Sai Xu; Xiuxiu Sun; Enli Lü; Huazhong Lu. A modified mean deviation threshold function based on fast Fourier transform and its application in litchi rest storage life recognition using an electronic nose. Journal of Food Measurement and Characterization 2017, 12, 867 -876.
AMA StyleSai Xu, Xiuxiu Sun, Enli Lü, Huazhong Lu. A modified mean deviation threshold function based on fast Fourier transform and its application in litchi rest storage life recognition using an electronic nose. Journal of Food Measurement and Characterization. 2017; 12 (2):867-876.
Chicago/Turabian StyleSai Xu; Xiuxiu Sun; Enli Lü; Huazhong Lu. 2017. "A modified mean deviation threshold function based on fast Fourier transform and its application in litchi rest storage life recognition using an electronic nose." Journal of Food Measurement and Characterization 12, no. 2: 867-876.
A regenerative braking system and hydraulic braking system are used in conjunction in the majority of electric vehicles worldwide. We propose a new regenerative braking distribution strategy that is based on multi-input fuzzy control logic while considering the influences of the battery’s state of charge, the brake strength and the motor speed. To verify the braking performance and recovery economy, this strategy was applied to a battery electric vehicle model and compared with two other improved regenerative braking strategies. The performance simulation was performed using standard driving cycles (NEDC, LA92, and JP1015) and a real-world-based urban cycle in China. The tested braking strategies satisfied the general safety requirements of Europe (as specified in ECE-13H), and the emergency braking scenario and economic potential were tested. The simulation results demonstrated the differences in the braking force distribution performance of these three regenerative braking strategies, the feasibility of the braking methods for the proposed driving cycles and the energy economic potential of the three strategies.
Boyi Xiao; Huazhong Lu; Hailin Wang; Jiageng Ruan; Nong Zhang. Enhanced Regenerative Braking Strategies for Electric Vehicles: Dynamic Performance and Potential Analysis. Energies 2017, 10, 1875 .
AMA StyleBoyi Xiao, Huazhong Lu, Hailin Wang, Jiageng Ruan, Nong Zhang. Enhanced Regenerative Braking Strategies for Electric Vehicles: Dynamic Performance and Potential Analysis. Energies. 2017; 10 (11):1875.
Chicago/Turabian StyleBoyi Xiao; Huazhong Lu; Hailin Wang; Jiageng Ruan; Nong Zhang. 2017. "Enhanced Regenerative Braking Strategies for Electric Vehicles: Dynamic Performance and Potential Analysis." Energies 10, no. 11: 1875.
This article develops a systematic model to study electric vehicle powertrain system efficiency by combining a detailed model of two-speed dual-clutch transmission system efficiency losses with an electric vehicle powertrain system model. In this model, the design factors including selection of the electric machine, gear ratios’ change, multi-plate wet clutch design, and gear shift schedule design are considered. Meanwhile, the application of detailed model for drag torque losses in the gearbox is discussed. Furthermore, the proposed model, developed with the MATLAB/Simulink platform, is applied to optimize/maximize the efficiency of the electric vehicle powertrain system using genetic algorithms. The optimization results demonstrate that the optimal results are different between simulations via New Europe Drive Cycle and Urban Dynamometer Driving Schedule, and comprehensive design and optimization of the powertrain system are necessary.
Yu Wang; Enli Lü; Huazhong Lu; Nong Zhang; Xingxing Zhou. Comprehensive design and optimization of an electric vehicle powertrain equipped with a two-speed dual-clutch transmission. Advances in Mechanical Engineering 2017, 9, 1 .
AMA StyleYu Wang, Enli Lü, Huazhong Lu, Nong Zhang, Xingxing Zhou. Comprehensive design and optimization of an electric vehicle powertrain equipped with a two-speed dual-clutch transmission. Advances in Mechanical Engineering. 2017; 9 (1):1.
Chicago/Turabian StyleYu Wang; Enli Lü; Huazhong Lu; Nong Zhang; Xingxing Zhou. 2017. "Comprehensive design and optimization of an electric vehicle powertrain equipped with a two-speed dual-clutch transmission." Advances in Mechanical Engineering 9, no. 1: 1.