Hyperspectral Detection of Moisture Content in Rice Straw Nutrient Bowl Trays Based on PSO-SVR
In the process of rice straw nutrient bowl tray drying, real-time detection of changes in moisture content to achieve automatic adjustment of drying factors is one of the important means to ensure its drying quality. At present, the main method for measuring the moisture content of rice straw nutrie...
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| Vydané v: | Sustainability Ročník 15; číslo 11; s. 8703 |
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27.05.2023
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| Abstract | In the process of rice straw nutrient bowl tray drying, real-time detection of changes in moisture content to achieve automatic adjustment of drying factors is one of the important means to ensure its drying quality. At present, the main method for measuring the moisture content of rice straw nutrient bowl trays is the drying and weighing method. This method is not only time consuming, labor intensive, and complex to operate, but also has poor real-time performance, which cannot meet the demand for real-time detection of the moisture content in the production process of rice straw nutrient bowl trays. In this regard, a real-time moisture content detection method for rice straw nutrient bowl trays based on hyperspectral imaging technology was studied. In this study we took the rice straw nutrient bowl tray during the drying process as the research object, adopted a single factor experiment, took microwave power, hot air temperature, and hot air speed as the drying factors, and took the moisture content of the rice straw nutrient bowl tray as the drying index. The rice straw nutrient bowl tray was dried according to the designed drying conditions. When drying, we removed the rice straw nutrient bowl tray every 5 min for weighing and collected hyperspectral image data within the wavelength range of 400~1000 nm until its quality remained unchanged. A total of 204 samples were collected. Using the average spectrum of the region of interest as the sample for effective spectral information, spectral preprocessing was performed using multivariate scattering correction (MSC), standardization normal variables (SNV), and Savitzky–Golay convolution smoothing (SG) methods. Principal component analysis (PCA) and competitive adaptive reweighting (CARS) methods were adopted for the dimensionality reduction of the spectral data. Three prediction models of rice straw nutrient bowl tray moisture content, namely random forest regression (RF), particle swarm optimization support vector regression (PSO-SVR), and XGBoost model were constructed using the reduced dimension spectral data. Finally, the performance of the model was compared using the coefficient of determination (R2) and mean square error (RMSE) as evaluation indicators. The research results indicate that the PCA-PSO-SVR model established based on SG method preprocessing has the best predictive performance, with a training set decision coefficient R2C of 0.984, a training set mean square error RMSE-C of 2.775, a testing set decision coefficient R2P of 0.971, and a testing set mean square error RMSE-P of 3.448. The model therefore has a high accuracy. This study achieved rapid detection of water content in rice straw nutrition trays. This method provides a reliable theoretical basis and technical support for the rapid detection of rice straw nutrient bowl tray moisture content, and is of great significance for improving the quality of rice straw nutrient bowl trays; promoting the popularization and application of raising rice straw nutrient bowl tray seedlings and whole process mechanized planting technology system; improving soil structure; and protecting the ecological environment. |
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| AbstractList | In the process of rice straw nutrient bowl tray drying, real-time detection of changes in moisture content to achieve automatic adjustment of drying factors is one of the important means to ensure its drying quality. At present, the main method for measuring the moisture content of rice straw nutrient bowl trays is the drying and weighing method. This method is not only time consuming, labor intensive, and complex to operate, but also has poor real-time performance, which cannot meet the demand for real-time detection of the moisture content in the production process of rice straw nutrient bowl trays. In this regard, a real-time moisture content detection method for rice straw nutrient bowl trays based on hyperspectral imaging technology was studied. In this study we took the rice straw nutrient bowl tray during the drying process as the research object, adopted a single factor experiment, took microwave power, hot air temperature, and hot air speed as the drying factors, and took the moisture content of the rice straw nutrient bowl tray as the drying index. The rice straw nutrient bowl tray was dried according to the designed drying conditions. When drying, we removed the rice straw nutrient bowl tray every 5 min for weighing and collected hyperspectral image data within the wavelength range of 400~1000 nm until its quality remained unchanged. A total of 204 samples were collected. Using the average spectrum of the region of interest as the sample for effective spectral information, spectral preprocessing was performed using multivariate scattering correction (MSC), standardization normal variables (SNV), and Savitzky–Golay convolution smoothing (SG) methods. Principal component analysis (PCA) and competitive adaptive reweighting (CARS) methods were adopted for the dimensionality reduction of the spectral data. Three prediction models of rice straw nutrient bowl tray moisture content, namely random forest regression (RF), particle swarm optimization support vector regression (PSO-SVR), and XGBoost model were constructed using the reduced dimension spectral data. Finally, the performance of the model was compared using the coefficient of determination (R2) and mean square error (RMSE) as evaluation indicators. The research results indicate that the PCA-PSO-SVR model established based on SG method preprocessing has the best predictive performance, with a training set decision coefficient R2C of 0.984, a training set mean square error RMSE-C of 2.775, a testing set decision coefficient R2P of 0.971, and a testing set mean square error RMSE-P of 3.448. The model therefore has a high accuracy. This study achieved rapid detection of water content in rice straw nutrition trays. This method provides a reliable theoretical basis and technical support for the rapid detection of rice straw nutrient bowl tray moisture content, and is of great significance for improving the quality of rice straw nutrient bowl trays; promoting the popularization and application of raising rice straw nutrient bowl tray seedlings and whole process mechanized planting technology system; improving soil structure; and protecting the ecological environment. In the process of rice straw nutrient bowl tray drying, real-time detection of changes in moisture content to achieve automatic adjustment of drying factors is one of the important means to ensure its drying quality. At present, the main method for measuring the moisture content of rice straw nutrient bowl trays is the drying and weighing method. This method is not only time consuming, labor intensive, and complex to operate, but also has poor real-time performance, which cannot meet the demand for real-time detection of the moisture content in the production process of rice straw nutrient bowl trays. In this regard, a real-time moisture content detection method for rice straw nutrient bowl trays based on hyperspectral imaging technology was studied. In this study we took the rice straw nutrient bowl tray during the drying process as the research object, adopted a single factor experiment, took microwave power, hot air temperature, and hot air speed as the drying factors, and took the moisture content of the rice straw nutrient bowl tray as the drying index. The rice straw nutrient bowl tray was dried according to the designed drying conditions. When drying, we removed the rice straw nutrient bowl tray every 5 min for weighing and collected hyperspectral image data within the wavelength range of 400~1000 nm until its quality remained unchanged. A total of 204 samples were collected. Using the average spectrum of the region of interest as the sample for effective spectral information, spectral preprocessing was performed using multivariate scattering correction (MSC), standardization normal variables (SNV), and Savitzky-Golay convolution smoothing (SG) methods. Principal component analysis (PCA) and competitive adaptive reweighting (CARS) methods were adopted for the dimensionality reduction of the spectral data. Three prediction models of rice straw nutrient bowl tray moisture content, namely random forest regression (RF), particle swarm optimization support vector regression (PSO-SVR), and XGBoost model were constructed using the reduced dimension spectral data. Finally, the performance of the model was compared using the coefficient of determination (R[sup.2]) and mean square error (RMSE) as evaluation indicators. The research results indicate that the PCA-PSO-SVR model established based on SG method preprocessing has the best predictive performance, with a training set decision coefficient R[sup.2]C of 0.984, a training set mean square error RMSE-C of 2.775, a testing set decision coefficient R[sup.2]P of 0.971, and a testing set mean square error RMSE-P of 3.448. The model therefore has a high accuracy. This study achieved rapid detection of water content in rice straw nutrition trays. This method provides a reliable theoretical basis and technical support for the rapid detection of rice straw nutrient bowl tray moisture content, and is of great significance for improving the quality of rice straw nutrient bowl trays; promoting the popularization and application of raising rice straw nutrient bowl tray seedlings and whole process mechanized planting technology system; improving soil structure; and protecting the ecological environment. |
| Audience | Academic |
| Author | Zhang, Zihan Qi, Lianxing Zhang, Xinyue Yu, Haiming Li, Haiyuan Hu, Yuhui Zhang, Kai Jiang, Jiwen |
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| Cites_doi | 10.1016/j.jfoodeng.2019.01.004 10.1016/j.infrared.2022.104118 10.1016/j.chemolab.2008.11.006 10.3389/fpls.2021.627865 10.3390/app11157152 10.1016/j.jobab.2020.07.001 10.1016/j.saa.2019.117515 10.1016/j.compstruct.2022.116599 10.1364/BOE.5.001378 10.1016/j.aca.2012.03.038 10.1007/s11269-017-1781-8 10.3390/su9112009 10.1016/j.ifset.2013.09.002 10.1080/02564602.2020.1740615 10.3390/agriculture10070292 10.1016/j.jfoodeng.2013.12.008 |
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| References | Shourian (ref_31) 2017; 31 Shi (ref_33) 2023; 306 Tian (ref_20) 2019; 50 Fearn (ref_22) 2009; 96 Zhang (ref_13) 2018; 49 Liang (ref_29) 2020; 225 Wang (ref_2) 2017; 48 Zhang (ref_10) 2019; 35 ref_30 Yu (ref_6) 2020; 42 Wei (ref_9) 2019; 248 Li (ref_12) 2021; 52 Benemaran (ref_36) 2023; 32 ref_18 Li (ref_25) 2021; 12 ref_15 Qian (ref_19) 2018; 49 Napoli (ref_28) 2014; 5 Bai (ref_21) 2017; 37 Hui (ref_23) 2016; 36 Yan (ref_27) 2022; 43 Chen (ref_34) 2022; 42 Qian (ref_1) 2019; 35 Yu (ref_7) 2013; 29 Uddin (ref_26) 2021; 38 Liu (ref_14) 2013; 20 Goodman (ref_3) 2020; 5 Wu (ref_16) 2012; 726 Huang (ref_17) 2014; 128 Dong (ref_11) 2022; 123 Bayrami (ref_32) 2022; 44 Chen (ref_24) 2019; 50 Yu (ref_4) 2020; 51 Qian (ref_35) 2022; 38 ref_5 Sun (ref_8) 2019; 39 |
| References_xml | – volume: 248 start-page: 89 year: 2019 ident: ref_9 article-title: Visual Detection of the Moisture Content of Tea Leaves with Hyperspectral Imaging Technology publication-title: J. Food Eng. doi: 10.1016/j.jfoodeng.2019.01.004 – volume: 39 start-page: 910 year: 2019 ident: ref_8 article-title: Visualization of Water Content Distribution in Potato Leaves Based on Hyperspectral Image publication-title: Spectrosc. Spectr. Anal. – ident: ref_30 – volume: 32 start-page: 583 year: 2023 ident: ref_36 article-title: Ensemble Deep Learning-Based Models to Predict the Resilient Modulus of Modified Base Materials Subjected to Wet-Dry Cycles publication-title: Geomech. Eng. – volume: 35 start-page: 285 year: 2019 ident: ref_10 article-title: Moisture content dectection in silage maize raw material based on hyperspectrum and improved discrete particle swarm publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 123 start-page: 104118 year: 2022 ident: ref_11 article-title: Quantitative Prediction and Visual Detection of the Moisture Content of Withering Leaves in Black Tea (Camellia Sinensis) with Hyperspectral Image publication-title: Infrared Phys. Technol. doi: 10.1016/j.infrared.2022.104118 – volume: 96 start-page: 22 year: 2009 ident: ref_22 article-title: On the Geometry of SNV and MSC publication-title: Chemom. Intell. Lab. Syst. doi: 10.1016/j.chemolab.2008.11.006 – volume: 48 start-page: 1 year: 2017 ident: ref_2 article-title: Comprehensive Utilization Status and Development Analysis of Crop Straw Resource in Northeast China publication-title: Trans. Chin. Soc. Agric. Mach. – volume: 43 start-page: 372 year: 2022 ident: ref_27 article-title: Principal Component Analysis and Cluster Analysis for Evaluating Amino Acid of Different Table Grapes (Vitis vinifera L.) Varieties publication-title: Sci. Technol. Food Ind. – volume: 35 start-page: 154 year: 2019 ident: ref_1 article-title: Advantages and disadvantages analysis of comprehensive utilization of straw in Jiangsu Province and countermeasure suggestions for collection-storage-transportation system publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 12 start-page: 627865 year: 2021 ident: ref_25 article-title: Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing publication-title: Front. Plant Sci. doi: 10.3389/fpls.2021.627865 – ident: ref_5 doi: 10.3390/app11157152 – volume: 38 start-page: 158 year: 2022 ident: ref_35 article-title: Extracting field-scale crop distribution in Lingnan using spatiotemporal filtering of Sentinel-1 time-series data publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 5 start-page: 143 year: 2020 ident: ref_3 article-title: Utilization of Waste Straw and Husks from Rice Production: A Review publication-title: J. Bioresour. Bioprod. doi: 10.1016/j.jobab.2020.07.001 – volume: 29 start-page: 40 year: 2013 ident: ref_7 article-title: Optimization of steam drying conditions for seedling-growing tray made of paddy-straw publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 42 start-page: 278 year: 2022 ident: ref_34 article-title: Quantitative Analysis of Carbon Content in Fly Ash Using LIBS Based on Support Vector Machine Regression publication-title: Acta Opt. Sin. – volume: 225 start-page: 117515 year: 2020 ident: ref_29 article-title: Prediction of Holocellulose and Lignin Content of Pulp Wood Feedstock Using near Infrared Spectroscopy and Variable Selection publication-title: Spectrochim. Acta Part A Mol. Biomol. Spectrosc. doi: 10.1016/j.saa.2019.117515 – volume: 52 start-page: 211 year: 2021 ident: ref_12 article-title: Detection of Moisture Content in Lettuce Canopy Based on Hyperspectral lmaging Technique publication-title: Trans. Chin. Soc. Agric. Mach. – volume: 49 start-page: 110 year: 2018 ident: ref_19 article-title: Optimization of Drying Process Parameters of Solar Herbage Dry Equipment publication-title: Trans. Chin. Soc. Agric. Mach. – volume: 50 start-page: 350 year: 2019 ident: ref_20 article-title: Detection of Anthocyanin Content of Purple Sweet Potato during Storage Period Based on Near Infrared Spectroscopy publication-title: Trans. Chin. Soc. Agric. Mach. – volume: 306 start-page: 116599 year: 2023 ident: ref_33 article-title: Improved Arithmetic Optimization Algorithm and Its Application to Carbon Fiber Reinforced Polymer-Steel Bond Strength Estimation publication-title: Compos. Struct. doi: 10.1016/j.compstruct.2022.116599 – volume: 42 start-page: 15 year: 2020 ident: ref_6 article-title: Simulation Analysis of Flow Field Uniformity in Air Distribution Room of Rice Seedbed Microwave Hot Air Coupling Dryer publication-title: J. Agric. Mech. Res. – volume: 51 start-page: 339 year: 2020 ident: ref_4 article-title: Drying Kinetic Model of Microwave Coupled with Hot Air Drying of Straw-based Nutrient Seedling-growing Bowl Tray publication-title: Trans. Chin. Soc. Agric. Mach. – volume: 49 start-page: 240 year: 2018 ident: ref_13 article-title: Inversion of Soil Moisture Content from Hyperspectra Based on Ridge Regression publication-title: Trans. Chin. Soc. Agric. Eng. – volume: 5 start-page: 1378 year: 2014 ident: ref_28 article-title: Hyperspectral and Differential CARS Microscopy for Quantitative Chemical Imaging in Human Adipocytes publication-title: Biomed. Opt. Express doi: 10.1364/BOE.5.001378 – volume: 726 start-page: 57 year: 2012 ident: ref_16 article-title: Rapid Prediction of Moisture Content of Dehydrated Prawns Using Online Hyperspectral Imaging System publication-title: Anal. Chim. Acta doi: 10.1016/j.aca.2012.03.038 – volume: 31 start-page: 4835 year: 2017 ident: ref_31 article-title: Performance Assessment of a Coupled Particle Swarm Optimization and Network Flow Programming Model for Optimum Water Allocation publication-title: Water Resour Manag. doi: 10.1007/s11269-017-1781-8 – ident: ref_18 doi: 10.3390/su9112009 – volume: 37 start-page: 3419 year: 2017 ident: ref_21 article-title: Near Infrared Spectrum Detection Method for Moisture Content of Populus Euphratica Leaf publication-title: Spectrosc. Spectr. Anal. – volume: 50 start-page: 200 year: 2019 ident: ref_24 article-title: Comprehensive Evaluation of Waste Water Quality Based on Quantitative Inversion Model Hyperspectral Technology publication-title: Trans. Chin. Soc. Agric. Mach. – volume: 20 start-page: 316 year: 2013 ident: ref_14 article-title: Non-Destructive Prediction of Salt Contents and Water Activity of Porcine Meat Slices by Hyperspectral Imaging in a Salting Process publication-title: Innov. Food Sci. Emerg. Technol. doi: 10.1016/j.ifset.2013.09.002 – volume: 38 start-page: 377 year: 2021 ident: ref_26 article-title: PCA-Based Feature Reduction for Hyperspectral Remote Sensing Image Classification publication-title: IETE Tech. Rev. doi: 10.1080/02564602.2020.1740615 – volume: 44 start-page: 375 year: 2022 ident: ref_32 article-title: Estimation of splitting tensile strength of modified recycled aggregate concrete using hybrid algorithms publication-title: Available SSRN 3992623 – ident: ref_15 doi: 10.3390/agriculture10070292 – volume: 128 start-page: 24 year: 2014 ident: ref_17 article-title: Prediction of Color and Moisture Content for Vegetable Soybean during Drying Using Hyperspectral Imaging Technology publication-title: J. Food Eng. doi: 10.1016/j.jfoodeng.2013.12.008 – volume: 36 start-page: 2111 year: 2016 ident: ref_23 article-title: Research on the Pre-Processing Methods of Wheat Hardness Prediction Model Based on Visible-Near Infrared Spectroscopy publication-title: Spectrosc. Spectr. Anal. |
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| SubjectTerms | Accuracy Moisture content Nutrition Raw materials Rice Soil fertility Soil structure |
| Title | Hyperspectral Detection of Moisture Content in Rice Straw Nutrient Bowl Trays Based on PSO-SVR |
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