Predicting the Tensile Strength of Plant Leaves Based on GA-SVM
Most plant material mechanics studies focus on the independent investigation of single properties, whereas systematic research on the coupling relationships among multidimensional performance indicators of plant leaves remains relatively scarce. This study employs a comprehensive testing instrument...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Journal of Natural Fibers |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15440478.2025.2514081 |
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| author | Wei Chang Meihong Liu Yayu Huang Junjie Lei Kai Wu |
| author_facet | Wei Chang Meihong Liu Yayu Huang Junjie Lei Kai Wu |
| author_sort | Wei Chang |
| collection | DOAJ |
| description | Most plant material mechanics studies focus on the independent investigation of single properties, whereas systematic research on the coupling relationships among multidimensional performance indicators of plant leaves remains relatively scarce. This study employs a comprehensive testing instrument to characterize the physical and chemical properties of plant leaves, tensile tests to assess tensile strength, and high-speed cameras to capture crack propagation during leaf fracture. Subsequently, correlation analysis is performed on the characteristic indicators, followed by dimensionality reduction using principal component analysis (PCA). A genetic algorithm (GA) is then applied to optimize the structural parameters of the support vector machine (SVM), establishing a GA-SVM-based predictive model for the tensile strength of plant leaves. A comparative analysis with other predictive algorithms demonstrates that the GA-SVM model achieves the lowest prediction error and highest accuracy, with mean absolute error and root mean squared error values of 0.0774 and 0.0745, respectively. Among the five models evaluated, the prediction accuracy ranks as follows: GA-SVM, SVM, decision tree, random forest, and back-propagation neural network. These findings fully validate the effectiveness and superiority of the GA-SVM model in predicting the tensile strength of plant leaves, providing a novel methodology for accurately predicting mechanical properties of plant materials. |
| format | Article |
| id | doaj-art-65d600d4ce774509842233c8fac992e8 |
| institution | Kabale University |
| issn | 1544-0478 1544-046X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Natural Fibers |
| spelling | doaj-art-65d600d4ce774509842233c8fac992e82025-08-20T03:26:43ZengTaylor & Francis GroupJournal of Natural Fibers1544-04781544-046X2025-12-0122110.1080/15440478.2025.2514081Predicting the Tensile Strength of Plant Leaves Based on GA-SVMWei Chang0Meihong Liu1Yayu Huang2Junjie Lei3Kai Wu4Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, ChinaR&D Center, Yunnan China Tobacco Industry Co., Ltd, Kunming, ChinaMost plant material mechanics studies focus on the independent investigation of single properties, whereas systematic research on the coupling relationships among multidimensional performance indicators of plant leaves remains relatively scarce. This study employs a comprehensive testing instrument to characterize the physical and chemical properties of plant leaves, tensile tests to assess tensile strength, and high-speed cameras to capture crack propagation during leaf fracture. Subsequently, correlation analysis is performed on the characteristic indicators, followed by dimensionality reduction using principal component analysis (PCA). A genetic algorithm (GA) is then applied to optimize the structural parameters of the support vector machine (SVM), establishing a GA-SVM-based predictive model for the tensile strength of plant leaves. A comparative analysis with other predictive algorithms demonstrates that the GA-SVM model achieves the lowest prediction error and highest accuracy, with mean absolute error and root mean squared error values of 0.0774 and 0.0745, respectively. Among the five models evaluated, the prediction accuracy ranks as follows: GA-SVM, SVM, decision tree, random forest, and back-propagation neural network. These findings fully validate the effectiveness and superiority of the GA-SVM model in predicting the tensile strength of plant leaves, providing a novel methodology for accurately predicting mechanical properties of plant materials.https://www.tandfonline.com/doi/10.1080/15440478.2025.2514081GA-SVMPlant LeavesPrediction AccuracyTensile StrengthPhysical and Chemical Properties遗传算法优化的支持向量机 |
| spellingShingle | Wei Chang Meihong Liu Yayu Huang Junjie Lei Kai Wu Predicting the Tensile Strength of Plant Leaves Based on GA-SVM Journal of Natural Fibers GA-SVM Plant Leaves Prediction Accuracy Tensile Strength Physical and Chemical Properties 遗传算法优化的支持向量机 |
| title | Predicting the Tensile Strength of Plant Leaves Based on GA-SVM |
| title_full | Predicting the Tensile Strength of Plant Leaves Based on GA-SVM |
| title_fullStr | Predicting the Tensile Strength of Plant Leaves Based on GA-SVM |
| title_full_unstemmed | Predicting the Tensile Strength of Plant Leaves Based on GA-SVM |
| title_short | Predicting the Tensile Strength of Plant Leaves Based on GA-SVM |
| title_sort | predicting the tensile strength of plant leaves based on ga svm |
| topic | GA-SVM Plant Leaves Prediction Accuracy Tensile Strength Physical and Chemical Properties 遗传算法优化的支持向量机 |
| url | https://www.tandfonline.com/doi/10.1080/15440478.2025.2514081 |
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