Improving the accuracy of soil organic carbon content prediction based on visible and near-infrared spectroscopy and machine learning

Liu

2022

Front. Plant Sci.

Wood density, as a key indicator to measure wood properties, is of weighty significance in enhancing wood utilization and modifying wood properties in sustainable forest management. Visible–near-infrared (Vis-NIR) spectroscopy provides a feasible and efficient solution for obtaining wood density by the advantages of its efficiency and non-destructiveness. However, the spectral responses are different in wood products with different moisture content conditions, and changes in external factors may cause the regression model to fail. Although some calibration transfer methods and convolutional neural network (CNN)-based deep transfer learning methods have been proposed, the generalization ability and prediction accuracy of the models still need to be improved. For the prediction problem of Vis-NIR wood density in different moisture contents, a deep transfer learning hybrid method with automatic calibration capability (Resnet1D-SVR-TrAdaBoost.R2) was proposed in this study. The disadvantage of overfitting was avoided when CNN processes small sample data, which considered the complex exterior factors in actual production to enhance feature extraction and migration between samples. Density prediction of the method was performed on a larch dataset with different moisture content conditions, and the hybrid method was found to achieve the best prediction results under the calibration samples with different target domain calibration samples and moisture contents, and the performance of models was better than that of the traditional calibration transfer and migration learning methods. In particular, the hybrid model has achieved an improvement of about 0.1 in both R2 and root mean square error (RMSE) values compared to the support vector regression model transferred by piecewise direct standardization method (SVR+PDS), which has the best performance among traditional calibration methods. To further ascertain the generalizability of the hybrid model, the model was validated with samples collected from mixed moisture contents as the target domain. Various experiments demonstrated that the Resnet1D-SVR-TrAdaBoost.R2 model could predict larch wood density with a high generalization ability and accuracy effectively but was computation consuming. It showed the potential to be extended to predict other metrics of wood.

Section: Methodsmentioning

confidence: 99%

Prediction approach of larch wood density from visible–near-infrared spectroscopy based on parameter calibrating and transfer learning

Liu

2022

Front. Plant Sci.

“…Additionally, SNV method is the same choice as Xu et al [30] which can be used to evaluate the performance of the SAFS. For the larch wood dataset, the 21-point Savitzky-Golay smoothing algorithm was used to eliminate noise [39], the 1st derivative of the spectral data was used to baseline correction [40], and then we eliminated the influence of particle size and scattering on the spectrum of the sample surface by combining SNV. Specifically, the high leverage value combined with the residual Student's t-test method [41] was used to screen the singular sample numbers of the four moisture content groups in the larch wood dataset.…”

Section: Data Processingmentioning

confidence: 99%

“…Finally, 13 samples of No. 6,12,20,22,27,38,39,41,44,45,47, 56, and 154 were eliminated, and a total of 168 samples of larch wood were obtained.…”

Section: Data Processingmentioning

confidence: 99%

Algorithm of Stability-Analysis-Based Feature Selection for NIR Calibration Transfer

et al. 2022

Sensors

For conventional near-infrared spectroscopy (NIR) technology, even within the same sample, the NIR spectral signal can vary significantly with variation of spectrometers and the spectral collection environment. In order to improve the applicability and application of NIR prediction models, effective calibration transfer is essential. In this study, a stability-analysis-based feature selection algorithm (SAFS) for NIR calibration transfer is proposed, which is used to extract effective spectral band information with high stability between the master and slave instruments during the calibration transfer process. The stability of the spectrum bands shared between the master and slave instruments is used as the evaluation index, and the genetic algorithm was used to select suitable thresholds to filter out the spectral feature information suitable for calibration transfer. The proposed SAFS algorithm was applied to two near-infrared datasets of corn oil content and larch wood density. Simultaneously, its calibration transfer performances were compared with two classical feature selection methods. The effects of different preprocessing algorithms and calibration transfer algorithms were also assessed. The model with the feature variables selected by the SAFS obtained the best prediction. The SAFS algorithm can simplify the spectral data to be transferred and improve the transfer efficiency, and the universality of the SAFS allows it to be used to optimize calibration transfer in various situations. By combining different preprocessing and classic feature selection methods with this, the sensitivity of the correlation between spectral data and component information are improved significantly, as well as the effect of calibration transfer, which will be deeply developed.

“…Other scholars have recorded and protected the spatial data of Chinese classical garden heritage through three-dimensional point cloud technology, making the corresponding data more comprehensive and accurate [ 22 ]. In addition, with the advancement of agricultural modernization, some scholars have applied the three-dimensional point cloud technology to the measurement of farmland crop population growth parameters, which overcomes the limitations of traditional crop parameter measurement and realizes the nondestructive, efficient, and high-precision measurement of crop growth parameters [ 23 ]. As shown in Figure 1 , the spatial features and models extracted by 3D point cloud technology.…”

Section: Related Workmentioning

confidence: 99%

Research on Rural Landscape Spatial Information Recording and Protection Based on 3D Point Cloud Technology under the Background of Internet of Things

Yue

Computational Intelligence and Neuroscience

Chen

2022

The village with historical and cultural accumulation not only is the witness of the interaction between human activities and natural environment but also contains a lot of intangible cultural heritage. It is an important research object in the history of human social development and has great reference value for the construction of modern Humayun settlements. With the continuous change of modern rural landscape, the traditional engineering drawing cannot meet the needs of people for spatial information measurement. There are some problems such as slow drawing, low precision, and poor benefit, so computer drawing must be used. Therefore, this paper puts forward the research on rural landscape spatial information recording and protection based on 3D point cloud technology under the background of Internet of Things and realizes the recording of rural landscape spatial information through the construction of 3D point cloud spatial information recording model. The experimental results show that the improved unit point cloud spatial information recording model has better feature extraction effect and feature point extraction efficiency, and can classify the point cloud more carefully. At the same time, the scene 3D point cloud classification method based on conditional random field can better deal with the interference factors in the outdoor landscape and show a better classification effect.