Conventional seeding monitors often exhibit diminished accuracy under challenging field conditions. To address this, this study introduces a novel monitoring system leveraging flexible pressure sensors integrated with a finger-clamp seed metering device. The core principle is that the passage of each seed-clamping finger over the seed outlet generates a distinct, continuous pressure signal profile. A sophisticated Signal Feature Identification Algorithm (SFIA) was developed that transforms this raw signal data into a one-dimensional image for analysis. By employing binarization and bilateral filtering, the SFIA effectively suppresses noise from field vibrations and extracts key topographical features, enabling precise quantification of seeding events through peak detection. The complete system, implemented using LabVIEW and Python, was rigorously evaluated in field trials. Under conventional tillage, the system achieved an overall monitoring accuracy of 96.55%, with reseeding and missed seeding detection accuracies of 98.96% and 98.55%, respectively. Critically, it maintained high performance in challenging no-till conditions, demonstrating 95.46% overall accuracy, with 98.35% for reseeding and 98.42% for missed seeding detection. This research validates a pressure-based sensing approach as a robust alternative to traditional methods, presenting a new technological pathway for developing high-precision seeding monitoring systems resilient to common agricultural interferences.

This study systematically investigated the evolution law of rheological properties in the anaerobic fermentation system of cow manure (CM) and corn straw (CS) mixtures at varying total solids (TS). Combined with computational fluid dynamics (CFD) simulations, the distribution characteristics of particles within fermentation system were revealed, illustrating the impact mechanisms of agitation strategies. Simulations demonstrated that higher agitation speeds and TS levels increase instantaneous power consumption while altering mixing and sedimentation dynamics. When TS was 6%, 120 r/min required more power than 100 r/min and 80 r/min, but 100 r/min reduced daily energy consumption by 4.30% and 3.19% compared to 80 r/min and 120 r/min. Under conditions of TS=6% and 8%, with maintained process stability, experimental groups increased energy output by 12.42% and 30.41% while reducing energy consumption by 13.60% and 3.39% versus control groups, demonstrating significant overall efficiency gains. Inversely, when TS was 10%, experimental groups decreased energy output by 0.34%, while increasing energy consumption. These findings prove that when TS is below 10%, optimized agitation strategies enable positive net energy output, establish an optimization scheme, and balance biogas production efficiency with economic feasibility.

Genotyping and phenotyping are critical for wheat breeding, with accurate phenotypic acquisition from potted wheat using 3D point cloud technology essential to overcoming bottlenecks from slow, inefficient processes. This study focuses on developing methods for accurate organ-level phenotypic data extraction from potted wheat plants using 3D point cloud techniques. The study exploited a multi-view acquisition system to construct point cloud datasets for wheat key growth stages and trained a network. Semantic (organ classification) and instance (organ segmentation) segmentation were performed on potted wheat to extract organ-level wheat point clouds. However, the imbalance in point cloud proportions among different wheat organs caused low semantic segmentation accuracy, and the interference from awns caused significant distortion in ear point clouds after instance segmentation. To address these issues, a class-related sampling strategy based on RandLA-Net was proposed, which balances various organ point clouds through a class-related sampling strategy. In addition, a geometric symmetry-based point cloud completion method was introduced to replenish the distorted wheat ear point cloud. Based on the segmented organ point clouds, phenotypic parameters were obtained using minimum bounding box and quadratic surface fitting methods. The results showed that semantic segmentation accuracy improved by 13.6% through class point balancing, and the determination coefficient for the extracted ear volume increased by 10.2% after point cloud completion. The obtained phenotypic parameters showed a strong correlation with manual measurements (determination coefficients ranging from 0.7737 to 0.9552). These phenotype acquisition methods provide accurate and practical phenotypic acquisition, supporting wheat optimization and superior gene screening.

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.

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.

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.

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.

Current research on garlic clove breaking primarily focuses on equipment development and optimization, with limited attention to elucidating the relationship between the breaking process and resultant damage or clove separation. To address this gap, this paper presents GarlicNet, a deep learning model that utilizes pressure signals generated during clove breaking to accurately detect breakage severity and separation extent. The model employs an enhanced adaptive channel attention mechanism to capture critical multi-channel information and a dual-branch attention structure to amplify salient features within pressure signals. Experimental results demonstrate high performance: GarlicNet achieved 95.6% accuracy, 95.9% recall, 95.9% precision, and 95.8% F1-score for breakage detection; corresponding values for separation detection were 96.5%, 96.5%, 96.5%, and 96.4%. Ablation studies confirm the efficacy of the Dynamic Multi-Channel Convolution Fusion (DMCF) module and Dual-Branch Attention Fusion (DAF) structure. Compared to benchmark models, GarlicNet exhibits superior detection performance and robustness. These findings validate the feasibility of predicting breakage and separation via pressure signal during clove breaking and underscore the model’s practical utility. This approach shows significant potential for mechanized garlic processing by reducing losses, improving efficiency, and advancing industrial automation.

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.

To mitigate the damage rate of Camellia oleifera seeds during the process of Camellia oleifera fruit dehulling, a Camellia oleifera fruit cutting and compression machine was developed. This machine operates on the principle that the shell-breaking stress of Camellia oleifera fruit is reduced following cutting, thereby integrating the steps of grading, cutting, compressing, and separating for the dehulling of Camellia oleifera fruit. Utilizing the electronic universal testing machine, a 4-factor single-factor test was conducted, including the number of cutting knives, cutting direction, compressing direction, and Camellia oleifera fruit size. The results showed that the average breaking force of Camellia oleifera seeds was 196.33 N. Compared with direct separating without cutting, the shell-breaking of Camellia oleifera fruit with 1, 2, 3, and 4 cuts decreased 30.14%, 38.62%, 45.05%, and 47.74%, respectively. Compared with cutting along the long axis, the shell-breaking force of Camellia oleifera fruit cutting along the minor axis decreased 8.1%; however, the effect of cutting direction on shell-breaking force is not significant. The shell-breaking force of Camellia oleifera fruit compressing along the long axis was 24.11% lower than that of Camellia oleifera fruit compressed along the minor axis. With the increase of Camellia oleifera fruit size, the shell-breaking force of Camellia oleifera fruit also increased. The shell-breaking force of 25-30 mm, 30-35 mm, and 35-40 mm was 85.62%, 127.34%, and 178.69% higher than that of 20-25 mm, respectively. In conclusion, using grading measures, making one cut along the long axis and compressing along the long axis is a suitable method for husking of Camellia oleifera fruit.