In this paper, hyperspectral imaging technology, combined with chemometrics methods, was used to detect the nitrogen content of soybean leaves, and to achieve the rapid, non-destructive and in situ detection of the nitrogen content in soybean leaves. Soybean leaves under different fertilization treatments were used as the research object, and the hyperspectral imaging data and the corresponding nitrogen content data of soybean leaves at different growth stages were obtained. Seven spectral preprocessing methods, such as Savitzky–Golay smoothing (SG), first derivative (1-Der), and direct orthogonal signal correction (DOSC), were used to establish the quantitative prediction models for soybean leaf nitrogen content, and the quantitative prediction models of different spectral preprocessing methods for soybean leaf nitrogen content were analyzed and compared. On this basis, successive projections algorithm (SPA), genetic algorithm (GA) and random frog (RF) were employed to select the characteristic wavelengths and compress the spectral data. The results showed the following: (1) The full-spectrum prediction model of soybean leaf nitrogen content based on DOSC pretreatment was the best. (2) The PLS model of soybean leaf nitrogen content based on the five characteristic wavelengths had the best prediction performance. (3) The spatial distribution map of soybean leaf nitrogen content was generated in a pixel manner using the extracted five characteristic wavelengths and the DOSC-RF-PLS model. The nitrogen content level of soybean leaves can be quantified in a simple way; this provides a foundation for rapid in situ non-destructive detection and the spatial distribution difference detection of soybean leaf nitrogen. (4) The overall results illustrated that hyperspectral imaging technology was a powerful tool for the spatial prediction of the nitrogen content in soybean leaves, which provided a new method for the spatial distribution of the soybean nutrient status and the dynamic monitoring of the growth status.

In order to analyze the relationship between hyperspectral image and soybean canopy nitrogen content in the field, and to establish a prediction model for soybean canopy nitrogen content with few parameters and a simple structure, hyperspectral image data and corresponding nitrogen content data of soybean canopy at different growth periods under different fertilization treatments were acquired. Three spectral characteristic variables selection methods, including correlation coefficient analysis, stepwise regression, and spectral index analysis, were used to determine the spectral characteristic variables that are closely related to the soybean canopy nitrogen content. The predictive models for soybean canopy nitrogen content based on spectral characteristic variables were established using a multiple linear regression algorithm. On this basis, the established prediction models for soybean canopy nitrogen content were compared and analyzed, and the optimal prediction model for soybean canopy nitrogen content was determined. To verify the applicability of prediction models for soybean canopy nitrogen content, a spatial distribution map of soybean canopy nitrogen content at the regional scale was drawn based on unmanned aerial vehicle (UAV) hyperspectral imaging data at the flowering and seed filling stages of soybean in the experimental area, and the spatial distribution of soybean nitrogen content was statistically analyzed. The results show the following: (1) Soybean canopy spectral reflectance was highly significantly negatively correlated with soybean canopy nitrogen content in the range of 450–729 nm, and highly significantly positively correlated in the range of 756–774 nm, with the largest positive correlation coefficient of 0.2296 at 765 nm and the largest absolute value of negative correlation coefficient of −0.8908 at 630 nm. (2) The predictive model for soybean canopy nitrogen content based on three optimal spectral indices, NDSI(R₅₅₂,R₅₅₅), RSI(R₅₃₇,R₅₇₃), and DSI(R₅₄₀,R₅₅₅), was optimal, with R² of 0.9063 and 0.91566 and RMSE of 3.3229 and 3.2219 for the calibration and prediction set, respectively. (3) Based on the established optimal prediction model for soybean canopy nitrogen content combined with the UAV hyperspectral image data, spatial distribution maps of soybean nitrogen content at the flowering and seed filling stages were generated, and the R² between soybean nitrogen content in the spatial distribution map and the ground measured value was 0.93906, the RMSE was 3.6476, and the average relative error was 9.5676%, which indicates that the model had higher prediction accuracy and applicability. (4) The overall results show that the optimal prediction model for soybean canopy nitrogen content established based on hyperspectral imaging data has the characteristics of few parameters, a simple structure, and strong applicability, which provides a new method for realizing rapid, dynamic, and non-destructive monitoring of soybean nutritional status on the regional scale and provides a decision-making basis for precision fertilization management during soybean growth.

In order to improve the adhesive and passing performance of agricultural tracked vehicles under a non-structural environment, a theoretical design method of the structure of a bionic track pattern is proposed in this article. The Saanen goat is taken as the experimental subject, and the hoof tips and hoof spheres are taken as the characteristic functional parts, whose pressure is measured by thin film pressure sensors. The Qualisys Track Manager (QTM) gait analysis system was used to obtain the gait sequence of goats under multi-slope. The changes in vertical ground reaction force (GRF) and vertical impulse (VI) of the hoof tips and spheres and adhesion coefficient under multi-slope were analyzed. The results show that with the increase in slope, the GRF is transferred from the left hind hoof to the right front hoof, and the right front hoof has the most significant effect. Under the 10-degree slope, the peak vertical GRF and VI of the inner tip of the right front hoof are the largest; peak vertical GRF is 146.20 N, and VI is 127.67 N·s. The adhesion coefficient is the largest; the right front and left hind hoof are in the diagonal two-phase supported state, and μ is 0.3455. Therefore, the inner tip of the right front hoof is used as a bionic prototype to design the track pattern architecture. It provides a theoretical basis for the design and optimization of bionic patterns applied to agricultural tracked vehicles.

Goats can walk freely and flexibly in complex environments such as concave and convex or soft ground. And their flexible spine has functions such as adjusting balance and providing auxiliary power during movement, while the limbs only have support functions. The spine has an adjustable and decisive role in the pressure on the sole of the hoof of the goat. Therefore, the goat spine was taken as the bionic prototype, the three-dimensional force distribution of the goat body space was analyzed, and the optimal spinal space curve was explored, combined with the goat gait cycle. Based on the study of spinal curve arrangement and placement, the spinal curve was stretched along the grouser length direction. The soil contact surface structure of the track monomer was constructed based on functional simulation. And the bionic structure of the track monomer with superior adhesion performance was explored. The results of simulation analysis and soil tank test both showed that the attachment performance of bionic structure was better than that of an ordinary structure. It showed that adding bionic curves to the contact surface of the track monomer could significantly improve the adhesion performance, and the bionic structure with a single bionic curve arranged on the complete contact surface of the track monomer had the best adhesion performance. Moreover, the adhesion of the optimal track monomer bionic structure was increased by 19.22 N compared with an ordinary structure in the soil tank test, which verified the superiority of the track monomer bionic structure design. It provides a new method and a new idea for improving the adhesion performance of tracked vehicle in hilly areas.

Soft electronics play a crucial role in wearable and implantable systems because of their biocompatibility, conformability, miniaturization, and so on. Such advantages have significantly enhanced the functionalities of wearable and implantable systems, improved human–machine interaction, and revealed the great potential to revolutionize future healthcare. This chapter will provide a comprehensive overview of the latest developments in soft electronics for wearable and implantable systems, including the working principles and structure designs of disparate soft e-skins, diverse applications of soft implantable sensors, and energy storage strategies comprising supercapacitors and batteries for wearable and implantable systems. Additionally, the remaining challenges and future directions for this rapidly developing field are discussed. <!----> <!----> <!----> <!---->

To investigate whether spectral information from the backside of soybean leaves can help to improve the accuracy of disease identification models, based on spectral data from the front and back surfaces of leaves, as well as fused spectral data derived from them, a classification model was established using machine learning algorithms in this study. The study first used a spectral acquisition system to obtain spectral information from the front and back surfaces of the leaves, respectively, and calculated the averages of the two types of data to generate fused spectral data from both surfaces. For the three types of spectral data mentioned above, the following four preprocessing methods were applied: Savitaky–Golay smoothing (SG), multiplicative scatter correction (MSC), standard normal variate (SNV), and second-order derivative (2nd Der). At the same time, five modeling methods—support vector machines (SVM), partial least squares discriminant analysis (PLS-DA), convolutional neural network (CNN), random forest (RF), and back propagation neural network (BPNN)—were introduced to establish classification models of soybean leaf diseases, with the aim of selecting the optimal model that achieves the highest identification accuracy in each type of data. The results of the study indicate the following: Among the classification models based on spectral data from the front surface of the leaves, the BPNN model constructed after SG smoothing preprocessing (SG-BPNN) performed the best, achieving recognition accuracy of 88.89% on the testing set. Among the models based on spectral data from the back surface of the leaves, the MSC-PLS-DA model was identified as the optimal model, achieving an accuracy of 98.61% on the testing set. Among the models based on fused spectral data from both front and back surfaces, the MSC-PLS-DA model also demonstrated optimal performance, achieving a classification accuracy of 100% on the testing set. Its accuracy was 11.11% higher than that of the best model using only front-surface data and 1.39% higher than that of the best model using only back-surface data, which verified the effectiveness of fused spectral information from the front and back surfaces of the leaves in improving the accuracy of the soybean disease classification models. Therefore, this study provides a new approach and theoretical basis for the non-invasive, efficient, and precise detection of soybean diseases, and offers valuable reference for promoting the practical application of spectroscopy in the diagnosis of agricultural diseases.