The compatibility between the morphological characteristics (MCs) of rapeseed bare-root seedlings and transplanters directly affects planting quality. To improve the adaptability of transplanters to the MCs of different rapeseed cultivars, this study focused on six winter rapeseed cultivars (A1: Huyou 17, A2: Huayouza 9, A3: Fengyou 520, A4: Zhongyou 108, A5: Zheyou 50, and A6: Huayouza 62). Five MCs—root length (RL), seedling height (SH), root diameter (RD), stem thickness (ST), and seedling width (SW)—were measured during the seedling ages (25-40 d). Multiple comparisons were conducted to identify cultivars with no significant differences (NSD, α=0.05) in MCs, while skewness and kurtosis were analyzed to assess temporal variations in MC distributions. Quadratic polynomial regression was employed to model the growth trends of MCs for each cultivar. The results showed that the skewness and kurtosis ranges of MCs were –0.65 to 1.16 and 1.62 to 6.28, respectively, indicating significant variability in growth symmetry and concentration both within and among cultivars. As seedling age increased, the number of cultivars with NSD in MCs progressively decreased (25 d: 5; 30 d: 4; 35 d: 3; 40 d: 2), but A3, A5, and A6 maintained consistent stability before 35 d. Based on MC statistical analysis, the key design parameters for the transplanter were determined as follows: flat belt width was 180 mm, positioning bar spacing was 70 mm, flexible belt width was 150 mm, clamping distance was 8 mm, seedling drop height was 180 mm, and planting depth was 40 mm. Field tests demonstrated that the transplanter exhibited superior compatibility with cultivars A3, A5, and A6, achieving seedling delivery success rates of 93.75% (A3), 93.23% (A5), and 92.71% (A6), along with significantly higher planting success rates compared to the control group (A1, A2, A4). This study provides a theoretical basis for optimizing the compatibility between transplanters and rapeseed bare-root seedlings, as well as guiding the structural design of transplanting machinery.

Cavity maintenance is critical for ensuring the early growth and development of transplanted crops. A dense soil layer formed by a non-circular gear–parallel four-bar drilling mechanism serves as the key link between the drilling process and cavity stability. To optimize the operating parameters of the drilling mechanism for enhanced soil–machine interaction, this study systematically analyzed its structural configuration and working principles. Based on the inverse effect of cavity expansion theory, an elasto-plastic mechanical model of cavity surrounding soil is established, and the critical condition for cavity maintenance is derived. Using measured soil mechanical parameters at different moisture contents combined with the critical condition, the critical shear stress undertaken by the dense soil layer is calculated to determine the optimization objectives. Key response indicators and influencing factors are identified through the elasto-plastic model. A quadratic orthogonal rotary central composite design is adopted in soil-bin tests to establish regression equations between cavity maintenance indicators and influencing factors. Response surface methodology is employed to analyze effect trends and interaction effects, and a multi-objective optimization method based on the regression model is proposed to obtain the optimal parameter combination. Experimental results demonstrate that the optimized drilling parameters significantly improve cavity stability and operational performance. This study provides a theoretical basis and practical guidance for optimizing the operating parameters of drilling mechanisms from the perspective of soil mechanics.

Climate change, resource limitations, and increasing global food demand are accelerating the need for efficient and sustainable agricultural management practices. Unmanned aerial vehicles (UAVs) have emerged as a transformative technology in precision agriculture (PA) because of their capability to provide high-resolution, real-time, and site-specific crop monitoring. This review critically examines recent advancements (2016–2025) in UAV-assisted PA, focusing on UAV platforms, sensing technologies, data acquisition systems, information fusion methods, and artificial intelligence (AI)-driven analytical frameworks. Particular emphasis is placed on applications including crop monitoring, disease and pest detection, weed mapping, irrigation management, soil assessment, yield estimation, phenotyping, and precision spraying. The review highlights that integrating RGB, multispectral, hyperspectral, thermal, and LiDAR sensors with machine learning (ML) and deep learning (DL) algorithms substantially improves monitoring accuracy, operational efficiency, and agricultural decision-making compared with conventional practices. Algorithms such as Random Forest (RF), Support Vector Machine (SVM), convolutional neural networks (CNNs), and YOLO-based models have demonstrated strong effectiveness in yield prediction, disease recognition, and weed discrimination. Despite these advancements, several challenges continue to limit large-scale implementation, including restricted flight endurance, payload limitations, environmental sensitivity, data-processing complexity, interoperability issues, and limited AI model transferability across different agricultural environments. Furthermore, model performance remains highly dependent on sensor configuration, dataset quality, and field-specific environmental conditions. Recent developments indicate rapid commercialization of UAV technologies together with emerging trends in edge AI, explainable AI (XAI), UAV–IoT integration, cloud-based analytics, and autonomous multi-UAV systems. Overall, this review identifies major technological advancements, key operational limitations, and future research directions required to support scalable, reliable, and climate-resilient UAV-assisted agricultural systems.

To address the issue of insufficient accuracy in the discrete element simulation model for cotton stalk -residual film mixtures, this paper employs the Hertz-Mindlin model with the JKR contact model to calibrate the contact parameters. First, significant parameters affecting the angle of repose were identified using Plackett-Burman experiments. Subsequently, the optimal parameter range was determined in conjunction with the steepest climb test. A regression model was constructed using Box-Behnken experiments to optimize the parameters, yielding the optimal parameter combination: cotton stalk-residual film rolling friction coefficient of 0.46, residual film-residual film rolling friction coefficient of 0.31, residual film-steel rolling friction coefficient of 0.47, and residual film-residual film JKR surface energy of 0.41 J/m². Simulated pile-up tests were conducted on a mixture of cotton stalks and residual film using these optimal parameter combinations. The results showed that the error between the simulated and measured angle of repose was only 3.06%, validating the reliability of the parameters and providing a basis for research into the interaction characteristics between cotton stalks and residual film during film recovery.

How to achieve precise and coordinated control of greenhouse microclimate factors under strong coupling and nonlinear conditions remains a key challenge in protected agriculture. To address this issue, this study integrates intelligent control and multi-objective optimization to regulate greenhouse temperature and humidity in a coordinated manner. A mechanistic model of a Venlo-type greenhouse was first developed in Matlab R2022a. Then, three control methods, namely LQR, MPC, and NMPC, were compared, and NMPC showed the best performance. Finally, NSGA-II was introduced to optimize the objective function weights of NMPC, further improving the control results. Compared with NMPC alone, the optimized method reduced the RMSE and MAE by 0.3366 and 0.0812 for temperature control, and by 0.2192 and 0.6853 for humidity control, respectively. The proposed method improves the precision and coordination of greenhouse environmental control and provides support for efficient greenhouse production. Ultimately, this study offers a promising technical paradigm for transitioning traditional greenhouse management towards highly autonomous and sustainable precision agriculture.

Accurate sex identification in Procambarus clarkii is essential for genetic breeding and aquaculture management, as it helps optimize population structure, improve reproductive efficiency, and support sustainable aquaculture development. However, manual identification is time-consuming, labor-intensive, and prone to errors, especially when subtle visual differences need to be distinguished. To address this problem, this study proposed SCM-DETR, a sex identification method for Procambarus clarkii based on multi-dimensional feature fusion and enhancement. A high-resolution imaging system was used to acquire two-dimensional images of Procambarus clarkii, and a labeled dataset, the Procambarus clarkii gonad dataset (PGD), was constructed. To improve identification performance, a multi-dimensional semantics and details fusion method (MSDM) was designed to integrate high-level semantic information with fine-grained detail features, thereby enhancing feature representation and localization accuracy. In addition, a channel-spatial focus network (CSFN) was introduced to capture discriminative multidimensional features, including texture and color, for more accurate identification of subtle sex-related differences. Experimental results showed that SCM-DETR-R18 achieved 95.8% mAP@0.50 and 64.6% mAP@0.50-0.95 on the PGD, improving by 1.9 and 1.1 percentage points over the baseline model, respectively. The AP values of female and male gonads reached 93.3% and 96.5%, with gains of 3.3 and 1.9 percentage points, respectively. Moreover, the proposed model had the lowest parameter count (21.11 M) among all compared methods. The results of this study demonstrate that SCM-DETR can effectively improve automated sex identification in Procambarus clarkii and has good potential for intelligent aquaculture applications.

As a key piece of agricultural mechanization equipment, harvesters play a crucial role in improving agricultural productivity and operational quality. With the increasing complexity of operating environments, especially under hilly and mountainous terrain and on wet, slippery soils, harvester slip has become an increasingly prominent problem. Excessive slip seriously impairs traction efficiency, energy consumption, operational stability, and working quality, making slip-ratio control a core technology for addressing this challenge. This paper reviews slip control methods for harvesters under complex terrain conditions, including slip-ratio observation and estimation, slip prediction, and control strategies. Different categories of slip control methods and their current applications are discussed in detail. Existing limitations are analyzed, and future research directions are proposed to provide new technical support for improving the efficiency and stability of harvesters in complex operating environments.

The process of deboning chicken feet has a significant impact on the quality and taste of chicken feet products. However, comprehensive models for its mechanical properties, particularly in terms of peel strength, are lacking. A TPA-based model for peel strength and related texture indicators, including hardness, chewiness, springiness, and adhesiveness, was developed, enabling comprehensive characterization of the mechanical behavior of chicken feet during the deboning process. Regression analysis and MATLAB-based grid search were used to optimize cooling temperature, heating time, and heating temperature, with peel strength as the main constraint. The optimal condition was identified as cooling at 5.0°C, heating for 8.00 min, and heating at 89.0°C. This study provides a quantitative optimization framework that integrates mechanical properties with texture evaluation, enabling precise control of deboning efficiency and product quality. The proposed method improves processing stability and offers a transferable modeling approach for other collagen-rich food materials in food engineering applications.

Weeds severely reduce rice yield and quality, making reliable in-field detection of weed and rice seedling essential for automated weed management. Although deep learning-based object detection techniques have shown significant potential in automatically distinguishing crops from weeds, existing models often suffer from large model sizes, high computational complexity, and insufficient feature extraction. To address these issues, this study proposes a lightweight multi-angle object detection model named MAL-YOLOv5 (Multi-Angle Lightweight YOLOv5), based on the YOLOv5 (You Only Look Once version 5) framework, which effectively reduces model complexity while maintaining detection performance. Specifically, the lightweight MobileNetV3 architecture is adopted to replace the original backbone network, significantly decreasing the number of parameters without compromising accuracy. Furthermore, the neck network of MAL-YOLOv5 is enhanced by integrating spatial and channel reconstruction convolution (SCConv) and a single-shot feature aggregation module (SCCSP), which reduces spatial and channel redundancies in the convolutional module, thereby compressing the neck network and improving feature representation. Additionally, a rotated bounding box with angular information is introduced for annotating and detecting rice seedling and weed, which effectively mitigates the interference from background and non-target objects, enabling more precise identification. Experimental results show that the precision, recall, and mAP of the MAL-YOLOv5 model are 93.1%, 91.9%, and 93.4%, respectively. Compared to YOLOv5s_obb, the MAL-YOLOv5 model reduces the number of parameters by 80.1% and computational cost in GFLOPs (Giga Floating-Point Operations) by 81.5%, significantly minimizing model size with only marginal loss in accuracy, conserving computational and storage costs while lowering the hardware requirements for intelligent mechanical weeding equipment.

To improve the efficiency and reduce the labor intensity of rhizobial inoculation, a variable-rate spraying and control system for variable rate application (VRA) was developed. This system uses an incremental Proportional Integral Derivative (PID) closed-loop control algorithm to accurately regulate the spraying rate according to the target application rate, making it particularly suitable for the precision spraying of small volumes. Laboratory tests on precise flow control showed the variation coefficient of the spraying nozzle did not exceed 1.2% within the pressure range of 0.10-0.20 MPa, and a clear linear relationship was observed between the spraying rate and pressure. For small-flow control, the maximum response time was 1.93 s, with an average of 1.62 s. Field trials at Heilongjiang Agricultural Xianghe Farm in China demonstrated that soybeans inoculated with rhizobia exhibited better average seed counts and 100-seed weights compared to a control group. When applied together with a full base fertilizer, the average yield with liquid rhizobial inoculant increased by 232.5 kg/hm², representing a 6.9% improvement over the control. These results clearly indicate that this spraying and control system for liquid rhizobial inoculation offers superior performance and provides important technical support for promoting widespread adoption of rhizobial technology in agricultural practice.