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 ${\omega }_1^{*}=0.099$, ${\omega }_2^{*}=0.202$, ${\omega }_3^{*}=0.000$, ${\omega }_4^{*}=0.357$, ${\omega }_5^{*}=0.088$, ${\omega }_6^{*}=0.089$, ${\omega }_7^{*}=0.000$, ${\omega }_8^{*}=0.165$, 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.

Existing plot seeders have several problems, e.g., seeding uniformity is poor, the breakage rate is higher, and so on. The design of a cone-grid format seed-metering device can both meet the experimental area’s seeding-operation requirements and the plant-to-row seeding requirements. We clarified the overall structure, working principle, and technical parameters of a novel seed-metering device. The gap-adjustment range of the seed-distributing mechanism was determined by analyzing the force on buckwheat seeds. A kinematic simulation using Simulink clarified that the seed-metering control mechanism is capable of realizing precise seed-discharge control. In this experiment, we used the layout of seed gaps, seed displacement, and the rotation rate as influencing factors, respectively. We determined the sowing uniformity coefficient of variation and the broken rate as indicators of the orthogonal experiment. The test results were then subjected to modeling analysis and optimization using Design-Expert12 software to determine the optimal operating parameters. Subsequently, validation experiments were conducted. The results show that the optimal configuration of parameters consisted of a layout seed gap of 13.42 mm, a seed displacement of 17.67 g, and a rotation rate of 15.59 min⁻¹. Under these optimal conditions, the coefficient of variation for sowing uniformity was measured at 14.56%, while the broken rate was 0.39%. The test results meet the technical requirements of buckwheat seed experimental-area sowing and plant-to-row sowing; for further research on buckwheat experimental-area breeding, this study provides a reference.

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.