Xiaolei Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xiaolei Wang has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Decong Zheng
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
3 shared publications - Dequan Liu
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
2 shared publications - Chao Zhang
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
1 shared publication - Jianlong Zhang
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
1 shared publication - Jiawei Wang
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
1 shared publication - Jiawei Wang
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China
1 shared publication
Latest Publications
- Open Access
Design and Experiments of a Roll-Knife Pickup for a Buckwheat Pickup Harvester
To reduce grain loss during pickup and prevent stalk entanglement in buckwheat harvesters, thereby improving the quality of mechanized harvesting, a three-stage pickup conveyor roll with a ground-level rotary knife-type pickup header was designed and tested. This paper, based on the growth characteristics of buckwheat, determined the three-stage pickup conveying process and the overall structure of the ground-level rotary knife-type pickup header. Kinematic analysis and parameter design of the rotary knife-type pickup roll were conducted. Finally, a physical prototype was fabricated, and field performance tests were carried out, using machine forward speed and pickup roll rotational speed as influencing factors and pickup loss rate as an evaluation metric. Results showed that the interaction between pickup roll rotational speed and forward speed had a significant effect on the pickup loss rate, with forward speed having a greater impact than pickup roll rotational speed. Under consideration of their interaction, when the pickup roll rotational speed was within the range of 396–457 r/min, and the forward speed was between 0.9–1.0 m/s, the pickup loss rate was minimized. Based on the regression equation model, the predicted optimal conditions were a forward speed of 1.0 m/s and a roll pickup speed of 396 rpm. Under these conditions, the test results showed a pickup loss rate of 5.235%, indicating good pickup performance. This research provides a reference for the design of pickup devices in grain combine harvesters.
Open AccessDesign and Experiments of a Roll-Knife Pickup for a Buckwheat Pickup Harvester
To reduce grain loss during pickup and prevent stalk entanglement in buckwheat harvesters, thereby improving the quality of mechanized harvesting, a three-stage pickup conveyor roll with a ground-level rotary knife-type pickup header was designed and tested. This paper, based on the growth characteristics of buckwheat, determined the three-stage pickup conveying process and the overall structure of the ground-level rotary knife-type pickup header. Kinematic analysis and parameter design of the rotary knife-type pickup roll were conducted. Finally, a physical prototype was fabricated, and field performance tests were carried out, using machine forward speed and pickup roll rotational speed as influencing factors and pickup loss rate as an evaluation metric. Results showed that the interaction between pickup roll rotational speed and forward speed had a significant effect on the pickup loss rate, with forward speed having a greater impact than pickup roll rotational speed. Under consideration of their interaction, when the pickup roll rotational speed was within the range of 396–457 r/min, and the forward speed was between 0.9–1.0 m/s, the pickup loss rate was minimized. Based on the regression equation model, the predicted optimal conditions were a forward speed of 1.0 m/s and a roll pickup speed of 396 rpm. Under these conditions, the test results showed a pickup loss rate of 5.235%, indicating good pickup performance. This research provides a reference for the design of pickup devices in grain combine harvesters.
- Open Access
Visible Near-Infrared Hyperspectral Soil Organic Matter Prediction Based on Combinatorial Modeling
Non-destructive, fast, and accurate prediction of soil organic matter content in farmland is of great significance for soil fertility assessment and rational fertilization. In the process of soil organic matter prediction, it is important to give full play to the advantages of different prediction models and to integrate different prediction models to innovatively construct a combined prediction model of soil organic matter content so as to improve the prediction accuracy and generalization ability of the model. In this study, the soil organic matter content of agricultural soils was taken as the research object, and the visible near-infrared hyperspectral curves of soils were measured by the Starter Kit indoor mobile scanning platform (Headwall Photonics, Bolton, MA, USA), and the original spectral curves were firstly de-noised by Savitzky–Golay (S-G) smoothing. Secondly, the smoothed and denoised spectral data were subjected to a first-order differential transform, and the features were selected based on the first-order differential transformed spectral data using the L1-paradigm algorithm features. Then, secondly, eight algorithms based on the selected feature bands, such as LASSO Regression (LASSO) (Model 1), Multilayer Perceptron (MLP) (Model 2), Random Forest (RF) (Model 3), Gaussian Kernel Regression (GKR) (Model 4), Ridge Regression (Model 5), Long Short-Term Memory (LSTM) (Model 6), Convolutional Neural Networks (CNN) (Model 7), and Support Vector Regression (SVR) (Model 8), were applied to construct a single-prediction model of soil organic matter content. Finally, a superior linear combination-prediction model was proposed by the eight single-prediction models constructed, and the standard deviation-based prediction validity was added to test the model. The results showed the following: (1) the weights of the eight single-prediction models in the combined prediction model were , , , , , , , , respectively; (2) The average precision E of the predicted values of soil organic matter content constructed based on the eight single-prediction models was 0.856; the average standard deviation σ was 0.181, and the average prediction validity M was 0.702; (3) The accuracy E of the predicted value of soil organic matter content of the combined model was 0.893, which was 4.322% higher than the average accuracy of the single model; the standard deviation of the combined model was 0.129, which was 28.333% lower than the average standard deviation of the single model, and the prediction validity M of the combined model was 0.778, which was 10.826% higher than the average prediction validity of the single model. The combined model can be used for the effective estimation of soil organic matter content in farmland with visible near-infrared spectral data, which can provide a basis and reference for the rapid detection of soil organic matter content in farmland.Open AccessVisible Near-Infrared Hyperspectral Soil Organic Matter Prediction Based on Combinatorial Modeling
Non-destructive, fast, and accurate prediction of soil organic matter content in farmland is of great significance for soil fertility assessment and rational fertilization. In the process of soil organic matter prediction, it is important to give full play to the advantages of different prediction models and to integrate different prediction models to innovatively construct a combined prediction model of soil organic matter content so as to improve the prediction accuracy and generalization ability of the model. In this study, the soil organic matter content of agricultural soils was taken as the research object, and the visible near-infrared hyperspectral curves of soils were measured by the Starter Kit indoor mobile scanning platform (Headwall Photonics, Bolton, MA, USA), and the original spectral curves were firstly de-noised by Savitzky–Golay (S-G) smoothing. Secondly, the smoothed and denoised spectral data were subjected to a first-order differential transform, and the features were selected based on the first-order differential transformed spectral data using the L1-paradigm algorithm features. Then, secondly, eight algorithms based on the selected feature bands, such as LASSO Regression (LASSO) (Model 1), Multilayer Perceptron (MLP) (Model 2), Random Forest (RF) (Model 3), Gaussian Kernel Regression (GKR) (Model 4), Ridge Regression (Model 5), Long Short-Term Memory (LSTM) (Model 6), Convolutional Neural Networks (CNN) (Model 7), and Support Vector Regression (SVR) (Model 8), were applied to construct a single-prediction model of soil organic matter content. Finally, a superior linear combination-prediction model was proposed by the eight single-prediction models constructed, and the standard deviation-based prediction validity was added to test the model. The results showed the following: (1) the weights of the eight single-prediction models in the combined prediction model were , , , , , , , , respectively; (2) The average precision E of the predicted values of soil organic matter content constructed based on the eight single-prediction models was 0.856; the average standard deviation σ was 0.181, and the average prediction validity M was 0.702; (3) The accuracy E of the predicted value of soil organic matter content of the combined model was 0.893, which was 4.322% higher than the average accuracy of the single model; the standard deviation of the combined model was 0.129, which was 28.333% lower than the average standard deviation of the single model, and the prediction validity M of the combined model was 0.778, which was 10.826% higher than the average prediction validity of the single model. The combined model can be used for the effective estimation of soil organic matter content in farmland with visible near-infrared spectral data, which can provide a basis and reference for the rapid detection of soil organic matter content in farmland. - Open Access
- 6 October 2023
- Published by MDPI AG in Agriculture
- Vol. 13 (10) , 1953
Design and Experiment of Double-Row Seed-Metering Device for Buckwheat Large Ridges
This article focuses on the low accuracy, poor stability, and other shortcomings of the traditional outer-groove buckwheat seed-metering wheel. A wheel-type large-ridge double-row seed-metering device with inner seed-filling holes was designed. The seed-metering device’s structural composition, working principle, and main technical parameters are clarified. The structural parameters of the seed-metering device shell and the seed-metering wheel are determined based on the force analysis, movement trajectory, and physical characteristics of the buckwheat grains. This experiment uses the JPS-12 metering device performance experiment bench for bench experimenting. The experiment uses the seed-metering device’s rotation speed, the seed position’s height, and the seed wheel’s aperture as experiment factors. Based on the experiment indicators of the qualified rate of number of holes and the grain damage rate, we used Design-Expert12 software to design single-factor, response surface, and verification experiments. The experiment results show that the best parameter combination is a seed-metering device rotation speed of 67 r/min, a seed position height of 115 mm, and a seed wheel aperture of 8 mm. In the optimal parameters, the qualified rate of the number of holes is 90.23%, and the grain damage rate is 0.62%. The experiment indicators meet the operational requirements of the buckwheat seeding device. The design and experimenting of the buckwheat large-ridge double-row seeding device provide a reference for further research on buckwheat seeding.
- 6 October 2023
- Published by MDPI AG in Agriculture
- Vol. 13 (10) , 1953
Open AccessDesign and Experiment of Double-Row Seed-Metering Device for Buckwheat Large Ridges
This article focuses on the low accuracy, poor stability, and other shortcomings of the traditional outer-groove buckwheat seed-metering wheel. A wheel-type large-ridge double-row seed-metering device with inner seed-filling holes was designed. The seed-metering device’s structural composition, working principle, and main technical parameters are clarified. The structural parameters of the seed-metering device shell and the seed-metering wheel are determined based on the force analysis, movement trajectory, and physical characteristics of the buckwheat grains. This experiment uses the JPS-12 metering device performance experiment bench for bench experimenting. The experiment uses the seed-metering device’s rotation speed, the seed position’s height, and the seed wheel’s aperture as experiment factors. Based on the experiment indicators of the qualified rate of number of holes and the grain damage rate, we used Design-Expert12 software to design single-factor, response surface, and verification experiments. The experiment results show that the best parameter combination is a seed-metering device rotation speed of 67 r/min, a seed position height of 115 mm, and a seed wheel aperture of 8 mm. In the optimal parameters, the qualified rate of the number of holes is 90.23%, and the grain damage rate is 0.62%. The experiment indicators meet the operational requirements of the buckwheat seeding device. The design and experimenting of the buckwheat large-ridge double-row seeding device provide a reference for further research on buckwheat seeding.