How does the choice of NIR feature selection method impact the accuracy of manure nutrient content estimation?
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The choice of NIR feature selection method significantly impacts the accuracy of manure nutrient content estimation. Different feature selection methods, such as Competitive adaptive reweighted sampling (CARS), Particle Swarm optimization (PSO), and Ant colony optimization (ACO), play a crucial role in enhancing the prediction accuracy of nutrient contents . For instance, in the study on manure sensing, the reflectance mode improved the calibration accuracy for NH4-N and Org-N, while the transflectance mode enhanced the predictability for other parameters like total solid (TS), ash, and particle size (PS). Similarly, in the research on soil organic carbon (SOC) estimation, the CARS method for selecting spectral feature bands, combined with Convolutional Neural Network (CNN) modeling, resulted in higher accuracy in predicting SOC content. Therefore, the selection of an appropriate feature selection method is crucial for improving the accuracy of manure nutrient content estimation.
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| Papers (4) | Insight |
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15 Citations | The choice of NIR feature selection method impacts the accuracy of soil total nitrogen estimation by Vis-NIR spectroscopy, crucial for precise manure nutrient content estimation. |
| The choice of NIR feature selection method impacts manure nutrient estimation accuracy; reflectance mode enhances NH4-N and Org-N prediction, while transflectance mode improves TS, ash, and PS estimation. | |
| The choice of NIR feature selection method impacts manure nutrient content estimation accuracy; Si-SVR model excels in accurate prediction of soil nitrogen, phosphorus, and potassium levels. | |
15 Citations | The choice of NIR feature selection method impacts manure nutrient content estimation accuracy; CARS outperformed SPA in selecting effective variables for soil nutrient prediction in the study. |
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