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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.

Soybean is one of the world’s major oil-bearing crops and occupies an important role in the daily diet of human beings. However, the frequent occurrence of soybean leaf diseases caused serious threats to its yield and quality during soybean cultivation. Rapid identification of soybean leaf diseases could provide a better solution for efficient control and subsequent precision application. In this study, a lightweight deep convolutional neural network (CNN) based on multiscale feature extraction fusion (MFEF) and combined with a dense connectivity (DC) network (MFEF-DCNet) was proposed for soybean leaf disease identification. In MFEF-DCNet, a multiscale feature extraction fusion (MFEF) module for soybean leaves was constructed by utilizing a convolutional attention module and depth-separable convolution to improve the model feature extraction capability. Multiscale features are fused by using dense connections (DC) in the backbone network to improve the model generalization capability. Experiments were implemented on eight distinct disease and deficiency classes of soybean images (including bacterial blight, cercospora leaf blight, downy mildew, frogeye leaf spot, healthy, potassium deficiency, soybean rust, and target spot) using the proposed network. The results showed that the MFEF-DCNet had an accuracy of 0.9470, an average precision of 0.9510, an average recall of 0.9480, and an F1-score of 0.9490 for soybean leaf disease identification. And MFEF-DCNet had certain performance advantages in terms of classification accuracy, convergence speed and other effects compared with VGG16, ResNet50, DenseNet201, EfficientNetB0, Xception and MobileNetV3_small models. In addition, the accuracy of the MFEF-DCNet model in recognizing soybean diseases in local data was 0.9024, which indicated that the MFEF-DCNet model had favorable application in practical applications. The proposed model and experience in this study could provide useful inspiration for automated disease identification in soybean and other crops.

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.

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.

Accurate recognition method of pitaya in natural environment provides technical support for automatic picking. Aiming at the intricate spatial position relationship between pitaya fruits and branches, a pitaya recognition method based on improved YOLOv4 was proposed. GhostNet feature extraction network was used instead of CSPDarkNet53 as the backbone network of YOLOv4. A structure of generating a large number of feature maps through a small amount of calculation was used, and the redundant information in feature layer was obtained with lower computational cost, which can reduce the number of parameters and computation of the model. Coordinate attention was introduced to enhance the extraction of fine-grained feature of targets. An improved combinational convolution module was designed to save computing power and prevent the loss of effective features and improve the recognition accuracy. The Ghost Module was referenced in Yolo Head to improve computing speed and reduce delay. Precision, Recall, F1, AP, detection speed and weight size were selected as performance evaluation indexes of recognition model. 8800 images of pitaya fruit in different environments were used as the dataset, which were randomly divided into the training set, the validation set and the test set according to the ratio of 7:1:2. The research results show that the recognition accuracy of the improved YOLOv4 model for pitaya fruit is 99.23%. Recall, F1 and AP are 95.10%, 98% and 98.94%, respectively. The detection speed is 37.2 frames·s -1 , and the weight size is 59.4MB. The improved YOLOv4 recognition algorithm can meet the requirements for the accuracy and the speed of pitaya fruit recognition in natural environment, which will ensure the rapid and accurate operation of the picking robot.

To meet rapid and non-destructive identification of selenium-enriched agricultural products selenium-enriched millet and ordinary millet were taken as objects. Image regions of interest (ROI) were selected to extract the spectral average value based on hyperspectral imaging technology. Reducing noise by the Savitzky-Golay (SG) smoothing algorithm, variables were used as inputs that were screened by successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), uninformative variable elimination (UVE), CARS-SPA, UVE-SPA, and UVE-CARS, while sample variables were used as outputs to build support vector machine (SVM) models. The results showed that the accuracy of CARS-SPA-SVM was 100% in the training set and 99.58% in the test set equivalent to that of CARS-SVM and UVE-CARS-SVM, which was higher than that of SPA-SVM, UVE-SPA-SVM, and UVE-SVM. Therefore, the method of CARS-SPA had superiority, and CARS-SPA-SVM was suitable to identify selenium-enriched millet. Finally, 454.57 nm, 484.98 nm, 885.34 nm, and 937.1 nm, which were obtained by wavelength extraction algorithms, were considered as the sensitive wavelengths of selenium information. This study provided a reference for the identification of selenium-enriched agricultural products.

Inspired by the obstacle avoidance mechanism of goose neck, a theoretical design method of bionic robotic arm was proposed to solve the contradiction between high flexibility and strong bearing capacity in narrow space. Taking the goose neck as the test object, a narrow space test environment with a width of 10 cm was built, and a 6 × 4 obstacle matrix was set up, to analyze the maximum value of joint angle, motion rate and trajectory in different target areas. The test results showed that the goose neck movement has continuity and transmissibility. The overall posture of the goose neck was adjusted through the synergistic movement of the anterior, middle and posterior segments to move toward the target position. The regulating effect of the anterior segment was significantly stronger than that of the middle and posterior segments. Specifically, the anterior segment of goose neck exhibited mostly transverse movement, with significant horizontal regulation; the middle segment of the goose neck was coupled with longitudinal movement, with similar movement ability in all directions, the posterior segment of the goose neck has mostly longitudinal movement, with significant height regulation. In addition, the YOLOv7-pose recognition network was used to recognize goose neck motion pose, which provides a new method for animal behavior research.

[This corrects the article DOI: 10.3389/fpls.2022.1030021.].

In this paper, reverse engineering technique was employed to extract the ridges of the hoof ball contour, and hoof ball tissue structure was analyzed based on the bionic prototype of goat hooves. The quantified geometric features were used to design the bionic track shoe pattern, which can enhance its adhesive performance and solve the problem that agricultural tracked vehicles in hilly and mountainous areas are prone to slip due to poor adhesive performance. The monolithic structure of the biomimetic goat hoof track shoe pattern and the ordinary one-line track pattern were arranged and combined; they included six kinds of track shoe models and the adhesive performance was compared. A discrete element system was established based on soil parameter determination to compare the maximum adhesion of different track shoe models. The bionic track shoe samples were prepared for soil bin tests to verify the reliability of the discrete element analysis results. Compared with the ordinary track shoe, the adhesion of the optimal bionic track shoe was improved by 9.1%.

To solve the technical problem that wheeled vehicles are prone to skidding on complex ground, due to poor adhesion performance, a tire-tread-structure design method based on the bionic principle is proposed in this paper. The 3D model of a goat’s foot was obtained using reverse engineering technology, and the curve equation was fitted by extracting the contour data of its outer-hoof flap edge, which was applied to the tire-tread-structure design. The bionic and herringbone-pattern rubber samples were manufactured, and a soil-tank test was carried out using an electronic universal tensile-testing machine, in order to verify the simulation results. The results showed that the overall adhesion of the bionic tread-pattern was greater than that of the normal tread-pattern with the same load applied and the same height and angle of the tread-pattern structure, and the maximum adhesion was increased by 14.23%. This research will provide a reference for optimizing the pattern structure and thus improving the passing performance of wheeled vehicles.