In order to improve the single-grain seeding rate of the pneumatic single seed metering device, an airflow seed cleaning device was designed in combination with positive pressure airflow. The influence of the position of the seed cleaning mechanism on the seed cleaning effect is theoretically analyzed and a flow field simulation test analysis of different nozzle structures was carried out by using Fluent software (ANSYS, Inc., Canonsburg, PA, USA). The results of this test show that a nozzle with a Witoszynski curve has good airflow concentration and uniform air pressure distribution. In order to verify the performance of the seed cleaning mechanism, a 0.7 times coated seed (hybrid rice Wuyou 1179) was used as the test material and a quadratic regression test with three levels was carried out with the rotation speed of the seed plate, the negative pressure of the suction chamber, and the positive pressure of the seed cleaning as the test factors. The results showed that when the speed of sucking plate was 30 r/min, the negative pressure of the suction chamber was 1.8 kPa and the positive pressure of the seed cleaning was 0.2 kPa; the seeding effect was at its best and the qualified rate of the seed metering device was the highest at 86.43%, the minimum leakage rate was 3.81%, and the multiple rate was 9.76%. The proposed seed cleaning mechanism effectively improves the accuracy of seeding and provides a certain theoretical basis for the single-grain sowing of hybrid rice.

Compared to traditional manual methods for assessing the cotton verticillium wilt (CVW) hazard level, utilizing deep learning models for foliage segmentation can significantly improve the evaluation accuracy. However, instance segmentation methods for images with complex backgrounds often suffer from low accuracy and delayed segmentation. To address this issue, an improved model, YOLO-VW, with high accuracy, high efficiency, and a light weight, was proposed for CVW hazard level assessment based on the YOLOv10n model. (1) It replaced conventional convolutions with the lightweight GhostConv, reducing the computational time. (2) The STC module based on the Swin Transformer enhanced the expression of foliage and disease spot boundary features, further reducing the model size. (3) It integrated a squeeze-and-excitation (SE) attention mechanism to suppress irrelevant background information. (4) It employed the stochastic gradient descent (SGD) optimizer to enhance the performance and shorten the detection time. The improved CVW severity assessment model was then deployed on a server, and a real-time detection application (APP) for CVW severity assessment was developed based on this model. The results indicated the following. (1) The YOLO-VW model achieved a mean average precision (mAP) of 89.2% and a frame per second (FPS) rate of 157.98 f/s in assessing CVW, representing improvements of 2.4% and 21.37 f/s over the original model, respectively. (2) The YOLO-VW model’s parameters and floating point operations per second (FLOPs) were 1.59 M and 7.8 G, respectively, compressed by 44% and 33.9% compared to the original YOLOv10n model. (3) After deploying the YOLO-VW model on a smartphone, the processing time for each image was 2.42 s, and the evaluation accuracy under various environmental conditions reached 85.5%, representing a 15% improvement compared to the original YOLOv10n model. Based on these findings, YOLO-VW meets the requirements for real-time detection, offering greater robustness, efficiency, and portability in practical applications. This model provides technical support for controlling CVW and developing cotton varieties resistant to verticillium wilt.

Rice direct seeding for bunch planting is a sustainable agricultural production method that reduces production costs, improves rice lodging resistance, and conserves irrigation water in the field. However, there are notable differences in seed treatment between direct seeding on dry land and in paddy fields, which can impact the seeding process’s accuracy. This study employs the numerical simulation methods of computational fluid dynamics (CFDs) and discrete element method (DEM) to examine the motion characteristics of dry and wet rice seeds in a fluid–solid coupled domain and their impact on seeding accuracy. The aim is to guide the optimization of the rice air-suction seed metering device. Rice seeds were divided into dry and wet groups, and their physical properties were measured. Discrete element models of rice seeds were constructed and calibrated using a polyhedral method. The results show that the static friction coefficient between the seed meter and the seed ranged from 0.902 to 0.950, and the thousand-grain weights ranged from 25.89 to 32.42 g, which were higher than those of the dry rice seed, which ranged from 0.774 to 0.839, and from 25.89 to 32.42 g. After calibration, the errors between the simulated dynamic stacking angles of HHZD, HYD, YLYD, HHZW, HYW, and YLYW and the physical–dynamic stacking angles were 0.12%, 0.13%, 0.75%, 0.62%, 0.08%, 0.75%, 0.59%, and 1.24%, respectively, which indicated that the discrete element model for rice was reliable. Additionally, a seeding accuracy test revealed that wet seeds of the same variety had higher missing and single indices, while dry seeds had higher triple and multiple indices. Furthermore, CFD-DEM simulations demonstrated that wet seeds’ normal and tangential forces were more significant than those on dry seeds during the seed-filling process. At 40 rpm, the normal and tangential forces during the seed-filling process of HYW are 37.69 × 10⁻³ N and 12.47 × 10⁻³ N, respectively, which are higher than those of HYD (25.18 × 10⁻³ N and 9.19 × 10⁻³ N). The action force of suctioned rice seeds was directly proportional to the missing and single indices. The primary factors contributing to the discrepancy in seeding accuracy between dry and wet rice are the thousand-grain weight, the static friction coefficient between the seed meter and the seed, and the action force exerted between the rice seeds. In addition, using a shaped hole structure and optimizing the seed chamber structure can reduce normal and tangential forces and improve seeding accuracy. This study provides a reference for the simulation of rice seed flow-solid coupling and optimization of air-suction seed metering devices.

When picking strawberries outdoors, due to factors such as light changes, obstacle occlusion, and small target detection objects, the phenomena of poor strawberry recognition accuracy and low recognition rate are caused. An improved YOLOv5n strawberry high-precision recognition algorithm is proposed. The algorithm uses FasterNet to replace the original YOLOv5n backbone network and improves the detection rate. The MobileViT attention mechanism module is added to improve the feature extraction ability of small target objects so that the model has higher detection accuracy and smaller module sizes. The CBAM hybrid attention module and C2f module are introduced to improve the feature expression ability of the neural network, enrich the gradient flow information, and improve the performance and accuracy of the model. The SPPELAN module is added as well to improve the model’s detection efficiency for small objects. The experimental results show that the detection accuracy of the improved model is 98.94%, the recall rate is 99.12%, the model volume is 53.22 MB, and the mAP value is 99.43%. Compared with the original YOLOv5n, the detection accuracy increased by 14.68%, and the recall rate increased by 11.37%. This technology has effectively accomplished the accurate detection and identification of strawberries under complex outdoor conditions and provided a theoretical basis for accurate outdoor identification and precise picking technology.

The identification of ecosystem types is important in ecological environmental assessment. However, due to cloud and rain and complex land cover characteristics, commonly used ecosystem identification methods have always lacked accuracy in subtropical urban agglomerations. In this study, China’s Guangdong–Hong Kong–Macao Greater Bay Area (GBA) was taken as a study area, and the Sentinel-1 and Sentinel-2 data were used as the fusion of active and passive remote sensing data with time series data to distinguish typical ecosystem types in subtropical urban agglomerations. Our results showed the following: (1) The importance of different features varies widely in different types of ecosystems. For grassland and arable land, two specific texture features (VV_dvar and VH_diss) are most important; in forest and mangrove areas, synthetic-aperture radar (SAR) data for the months of October and September are most important. (2) The use of active time series remote sensing data can significantly improve the classification accuracy by 3.33%, while passive time series remote sensing data improves by 4.76%. When they are integrated, accuracy is further improved, reaching a level of 84.29%. (3) Time series passive data (NDVI) serve best to distinguish grassland from arable land, while time series active data (SAR data) are best able to distinguish mangrove from forest. The integration of active and passive time series data also improves precision in distinguishing vegetation ecosystem types, such as forest, mangrove, arable land, and, especially, grassland, where the accuracy increased by 21.88%. By obtaining real-time and more accurate land cover type change information, this study could better serve regional change detection and ecosystem service function assessment at different scales, thereby supporting decision makers in urban agglomerations.