Classification of Lingwu long jujube internal bruise over time based on visible near-infrared hyperspectral imaging combined with partial least squares-discriminant analysis

Yuan, Ruirui; Liu, Guishan; He, Junfeng; Wan, Guoling; Li, Yue; Sun, Yourui

doi:10.1016/j.compag.2021.106043

Cited by 65 publications

(25 citation statements)

References 50 publications

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“…Therefore, it is necessary to preprocess the fluorescence spectrum data. In this study, three algorithms, namely de-trending (DT) [15,16], moving average (MA) [17], and Savitzky-Golay smoothing (S-G) [18], were selected to preprocess the raw fluorescence spectrum data. Both S-G and MA can improve the smoothness of spectral data.…”

Section: Preprocessing Methods Of Spectral Datamentioning

confidence: 99%

Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Wang

Chen

et al. 2022

Agriculture

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Non-destructive detection of the pH value of kiwifruit has important practical significance for its quality classification. In this study, hyperspectral fluorescence imaging technology was proposed to quantitatively predict the pH value of kiwifruit non-destructively. Firstly, the SPXY algorithm was used to divide samples into training and prediction sets and three different algorithms were used to preprocess the raw spectral data. Secondly, algorithms such as the iteratively retaining information variables (IRIV), the variable iterative space shrinkage approach (VISSA), the model adaptive space shrinkage (MASS), the random frog (RF), and their combination (i.e., IRIV + VISSA + MASS + RF, IVMR) were used to extract effective variables from the preprocessed spectral data. Moreover, the second extractions, such as IRIV-VISSA and IRIV-MASS, and the third extraction (i.e., IVMR-VISSA-IRIV) were used to further reduce the redundant variables. Based on the effective variables, four regression models—random forest (RF), partial least square (PLSR), extreme learning machines (ELM), and multiple-kernel support vector regression (MK-SVR)—were built and compared for predicting. The results show that IVMR-VISSA-IRIV-MK-SVR had the best prediction results, with RP2, RC2 and RPD of 0.8512, 0.8580, and 2.66, respectively, which verifies that hyperspectral fluorescence imaging technology is reliable for predicting the pH value of kiwifruit non-destructively.

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Section: Preprocessing Methods Of Spectral Datamentioning

confidence: 99%

Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Wang

Chen

et al. 2022

Agriculture

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“…Soil available phosphorus (AP) and soil available potassium were determined as described by Wang, Wang & Ma (2022) . Nitrate (NO 3 − -N) and ammonium (NH 4 + -N) nitrogen were analyzed by a continuous flow analytical system (AA3: SEAL Company, Röttenbach, Bavaria, Germany) ( Yuan et al, 2021 ). The soil pH as well as EC was determined as described by Bao (2000) .…”

Section: Methodsmentioning

confidence: 99%

Composition and environmental interpretation of the weed communities in the main planting base of jujube (Ziziphus jujubaMill. cv. ‘LingwuChangzao’), Ningxia province of China

Wang

Cao

Zou

et al. 2022

PeerJ

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Background Ziziphus jujuba Mill. cv. ‘LingwuChangzao’ is a traditional jujube cultivar in northwest China. It is of great significance to explore the weed community composition and environmental characterization for the ecological control and comprehensive management of weeds in jujube orchards. In this article, a total of 37 species were recorded in 40 sample plots (1 m × 1 m). Moreover, fourteen environmental indicators to characterize the spatial locations, climate and soil nutrient characteristics of the plant communities were adopted. Methodology Through the two-way indicator species analysis (TWINSPAN) quantity classification and canonical correspondence analysis (CCA) ranking methods, the types of weed communities in the main planting base of jujube ‘LingwuChangzao’ and the main environmental factors affecting the change and distribution of weed types were analyzed. Results The weed communities within the study area were divided into 15 types by the TWINSPAN classification. There were significant differences in soil factors to the species diversity indices of the weed communities, the diversity of weed communities was negatively correlated with available potassium, whereas positively correlated with soil water content. The CCA results showed that community structure and spatial distribution of weed communities were affected by soil water content, total potassium, soil organic carbon, total phosphorus, total nitrogen. Our results can be used as a reference for orchard weed management and provide a theoretical basis for weed invasion control and creating a higher biodiversity in arable land under the background of environmental change.

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“…6, it can be seen that the models misjudge each other when they are used to discriminate the three categories of storage time at 8 h, 24 h and 48 h. It can be seen from Table 1 that the best discriminant effect of the three models established based on spectral characteristics is the XGBoost model, with the overall accuracy of 77.50%, and the overall accuracy of RF and SVM models is 73.75% and 66.25%, respectively. Yuan et al (2021) established a PLS-DA model based on the spectra of Lingwu jujube at five time points (2 h, 4 h, 8 h, 12 h, and 24 h) after bruising, and the prediction set accuracy of the model was 90.00% at the original spectra. Although the results of this study are satisfactory in discriminating the storage time of bruised Lingwu, it isn't sure that the storage time of all fruits after bruising can be well distinguished based on spectral data, so, in this study, the spectral features combined with image features are used to achieve better discrimination.…”

Section: Modeling Based On Spectral Featuresmentioning

confidence: 99%

“…And the recognition rate of both prediction sets reached 93.75% in the discriminant analysis models based on the full spectrum and the feature wavelengths. Yuan et al (2021) used the hyperspectral technique to collect five time-point average spectra of Lingwu long jujube. Based on raw data and pre-processed spectra, a partial least squares discriminant analysis (PLS-DA) classification model was established; then, various variable selection methods were used to select the feature variables; and finally, a PLS-DA model based on the feature variables was developed; the results showed that the hyperspectral imaging technology combining with PLS-DA could distinguish bruise's Lingwu long jujube at different storage times.…”

Section: Introductionmentioning

confidence: 99%

Detection storage time of mild bruise’s yellow peaches using the combined hyperspectral imaging and machine learning method

Li¹,

Yin²,

Liu³

et al. 2022

J Anal Sci Technol

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To deduce the process of bruise and reduce the number of bruised fruits from the source, the storage time of yellow peaches after bruise should be identified. In order to distinguish the different storage times of mild bruise’s yellow peaches more effectively than current detection methods, the combined hyperspectral imaging and machine learning method was proposed. Firstly, the sample bruise region spectrum was extracted as spectral features, and then, the hyperspectral images were processed by Principal Component Analysis (PCA), and eight single-wavelength images were selected according to the weight coefficient curve of PC1 images, and the gray values of the selected images were calculated as image features. Finally, in order to find the optimal discriminative model, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost) models were built based on spectral features, image features, and spectral features combined with image features, respectively. The results show that the XGBoost models based on spectral features, image features, and spectral features combined with image features are the optimal models with the overall accuracy of 77.50%, 87.50% and 90.00%, respectively. To simplify the model, Competitive Adaptive Reweighted Sampling (CARS) algorithm was used to screen the wavelength of the normalized spectral data, and then they were fused with the image feature data again and the XGBoost model with an overall model accuracy of 95.00% was built. To sum up, the combined hyperspectral imaging and machine learning method can be used to distinguish the different storage times (2 h, 8 h, 24 h and 48 h) of mild bruise’s yellow peaches effectively. It provides a certain theoretical basis for hyperspectral imaging technology in fruit bruise detection.

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Classification of Lingwu long jujube internal bruise over time based on visible near-infrared hyperspectral imaging combined with partial least squares-discriminant analysis

Cited by 65 publications

References 50 publications

Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Non-Destructive Detection of pH Value of Kiwifruit Based on Hyperspectral Fluorescence Imaging Technology

Composition and environmental interpretation of the weed communities in the main planting base of jujube (Ziziphus jujubaMill. cv. ‘LingwuChangzao’), Ningxia province of China

Detection storage time of mild bruise’s yellow peaches using the combined hyperspectral imaging and machine learning method

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