Maize small leaf spot (Bipolaris maydis) is one of the most important diseases of maize. The severity of the disease cannot be accurately identified, the cost of pesticide application increases every year, and the agricultural ecological environment is polluted. Therefore, in order to solve this problem, this study proposes a novel deep learning network DISE-Net. We designed a dilated-inception module instead of the traditional inception module for strengthening the performance of multi-scale feature extraction, then embedded the attention module to learn the importance of interchannel relationships for input features. In addition, a dense connection strategy is used in model building to strengthen channel feature propagation. In this paper, we constructed a data set of maize small leaf spot, including 1268 images of four disease grades and healthy leaves. Comparative experiments show that DISE-Net with a test accuracy of 97.12% outperforms the classical VGG16 (91.11%), ResNet50 (89.77%), InceptionV3 (90.97%), MobileNetv1 (92.51%), MobileNetv2 (92.17%) and DenseNet121 (94.25%). In addition, Grad-Cam network visualization also shows that DISE-Net is able to pay more attention to the key areas in making the decision. The results showed that the DISE-Net was suitable for the classification of maize small leaf spot in the field.

Accurate acquisition of vehicle dynamics state information is essential for vehicle active safety control systems. However, these states cannot be easily measured, and the measurement is expensive. Conventional Kalman filters perform well for vehicle state estimation in Gaussian environments but exhibit low accuracy and robustness under practical non-Gaussian noise. Vehicle model parameter ingestion, inaccurate tire force calculation, and non-Gaussian noise from on-board sensors cause great challenges to the estimation of vehicle driving states. Therefore, this paper presents a robust hierarchical estimation scheme for vehicle driving state based on the maximum correntropy square-root cubature Kalman filter (MCSCKF) using easily measurable on-board sensor information. First, the vehicle mass is dynamically updated based on the recursive least squares (FRLS) method with a forgetting factor. Then, an adaptive sliding mode observer (ASMO) is designed to estimate the longitudinal and lateral tire forces. Ultimately, the vehicle states are estimated based on the MCSCKF under non-Gaussian noise. Two typical operating situations are carried out to verify the validity of the proposed estimation scheme. The results prove that the proposed estimation scheme can estimate the vehicle’s driving state accurately compared to other common methods. And the MCSCKF algorithm has better accuracy and robustness than the traditional Kalman filters for vehicle state estimation in non-Gaussian situations.

The detection of the impurity rate in machine-picked seed cotton is crucial for precision agriculture. This study proposes a novel Cotton-YOLO-Seg cotton-impurity instance segmentation algorithm based on the you only look once version 8 small segmentation model (Yolov8s-Seg). The algorithm achieves precise pixel-level segmentation of cotton and impurities in seed cotton images and establishes a detection model for the impurity rate, enabling accurate detection of the impurity rate in machine-picked cotton. The proposed algorithm removes the Pyramid 4 (P4) feature layer and incorporates Multi-Scale Convolutional Block Attention (MSCBCA) that integrates the Convolutional Block Attention Module (CBAM) and Multi-Scale Convolutional Attention (MSCA) into the Faster Implementation of Cross Stage Partial Bottleneck with 2 Convolutions (C2f) module of the feature extraction network, forming a novel C2f_MSCBCA module. The SlimNeck structure is introduced in the feature fusion network by replacing the P4 feature layer with the small-target detection layer Pyramid 2 (P2). Additionally, transfer learning is employed using the Common Objects in Context (COCO) instance segmentation dataset. The analysis of 100 groups of cotton image samples shows that the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) for impurity rate detection are 0.29%, 0.33%, and 3.70%, respectively, which are reduced by 52.46%, 48.44%, and 53.75% compared to the Yolov8s-seg model. The Precision (P), Recall (R), and mean Average Precision at an intersection over union of 0.5 (mAP@0.5) are 85.4%, 78.4%, and 80.8%, respectively, which are improved by 4.2%, 6.2%, and 6.4% compared to Yolov8s-seg model, significantly enhancing the segmentation performance of minor impurities. The Cotton-YOLO-Seg model demonstrates practical significance for precisely detecting the impurity rate in machine-picked seed cotton.

This study aimed to investigate the wear failure changes of spindle hook teeth and the reasons for such failure during field work. Spindle samples were obtained from a fixed position of the spindle bar under different field picking area conditions and combined with the spatial distribution characteristics of cotton bolls in Xinjiang. After cutting a spindle sample, a scanning electron microscope and an energy spectrum analyzer were used to characterize the micromorphology and element composition of the hook tooth surface and cross section under different working area conditions. The wear parameters of the hook teeth were then extracted. The results showed that the thickness of the coating on the surface of the hook tooth used in this study was between 66.1 µm and 74.4 µm. The major chemical element was chromium, with a small amount of nickel. During the field picking process, failure of the coating on the surface of the hook teeth initially appeared on the tooth tip and tooth edge, and then spread to the entire hook tooth surface. The wear failure of the hook teeth resulted from abrasive wear, oxidative wear, and fatigue peeling. As the picking area increased, the wear area of the hook teeth increased exponentially, while the wear width increased linearly. When the field picking area reached 533.33 ha, the maximum change rate of the wear area was 2.33 × 10³ µm²/ha, and the wear width was 1.84 µm/ha. During field work, the thickness of the coating decreased from the cutting surface to the tooth edge, and the wear rate gradually increased. The wear rate at Position 1 was the slowest, at 0.01 µm/ha, and the wear rate at Position 5 was the fastest, at 0.25 µm/ha.

Seed cotton compression molding solves the inconvenience of seed cotton transportation and storage after mechanical harvesting. Stress relaxation is closely related to the performance of the compressed seed cotton. In this study, an electronic universal testing machine with a homemade compression device was used to study the stress relaxation characteristics of machine-harvested seed cotton. The stress relaxation model of machine-harvested seed cotton was established, the influence of test factors on the response indexes was analyzed and, finally, stress relaxation characteristics of machine-harvested seed cotton were simulated. Results show that machine-harvested seed cotton stress relaxation characteristics can be described by the five-element Maxwell model. The equilibrium elastic modulus is negatively correlated with moisture content and cross-section dimensions, and the equilibrium elastic modulus is positively correlated with trash content and compression density. The rapid decay time and the residual stress ratio are negatively correlated with moisture content and compression density, but the influence of trash content and cross-section dimensions are limited. The stress relaxation process of machine-harvested seed cotton was simulated using virtual prototype technology, and the maximum error between the experimental and simulated values was obtained as 4.96%. The feasibility of the virtual prototype technique for the viscoelastic simulation of biomaterials was demonstrated.