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.

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 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.

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.

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.

Vegetable plug seedlings form root-substrate composites characterized by granular discreteness and cohesive bonding, which makes the direct measurement of contact parameters difficult and limits accurate simulation of transplanting processes. This study calibrated the key contact parameters and developed a discrete element method (DEM) model for broccoli plug seedling root-substrate composites by integrating physical experiments with EDEM simulations. Root shear tests, substrate angle of repose, sliding friction, and direct shear tests were performed to determine intrinsic mechanical properties. Using Plackett-Burman screening, steepest ascent, and Box-Behnken designs, the optimal combination of root static friction coefficient, critical stress, and bonding radius was obtained, with a relative error of only 0.70% between simulated and measured shear forces. For the substrate, the calibrated contact parameters of substrate-substrate and substrate-steel interactions yielded relative errors of 2.46% and 2.30%, respectively, while the simulated internal friction angle differed by only 2.64% from experimental values. The final composite model, validated through compression tests, showed a yield limit error of 4.22% and closely matched the deformation behavior observed in experiments. These results demonstrate that the proposed DEM model accurately captures the coupled mechanical behavior of flexible roots and cohesive substrates, providing a reliable tool for visual force analysis during transplanting and supporting the design optimization of seedling-picking and soil-seedling interaction mechanisms.

Machine transplanting as an advanced technology in rice cultivation has been widely adopted. Nevertheless, under extreme wind conditions, transplanted rice seedlings can float and then die off due to external forces generated by wind and water waves. Studying the mechanical characteristics of these seedlings is essential for understanding the underlying mechanisms of rice seedling floating. In this study, the Volume of Fluid (VOF) method is applied to study the flow mechanisms of water waves around rice seedlings. It is found that the pressure differential between the windward and leeward wave sides of the rice seedlings changes during wave diffraction, resulting in unsteady drag forces. The drag force reaches its maximum value when the wave crest propagates to the rice seedlings. Under the condition where waves do not break, increasing wave height does not alter the fluctuation pattern of drag force but leads to a larger drag force amplitude. When the wave height increases sufficiently to cause wave breaking, additional loads are generated, resulting in a significant increase in drag amplitude and exciting new high-frequency disturbances. Increasing wavelength reduces the drag force amplitude but raises its fundamental fluctuation frequency and introduces new low-frequency pulsations. As the rice seedlings tilt, the altered flow mechanism has an uncertain effect on the variation in drag force amplitude and generates new excitations with other frequencies. It indicates that increased wave height, decreased wavelength, and rice seedling inclination may all elevate the risk of rice seedlings lodging. These results could provide valuable references for the prevention of floating rice seedlings.

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.

To address the issue of insufficient accuracy and stability in pneumatic peanut planters during high-speed operations (above 8 km/h), this study designs a peanut precision planter based on combined positive and negative pressure, and improves its performance via multi-dimensional optimization. The research uses Shandong “Silihong” peanuts as the study subject, determining their geometric characteristics and designing the core parameters of the seed-metering disc: diameter of 255 mm, 21 suction holes, and suction hole diameter of 6.2 mm. A comparative simulation using EDEM discrete element method between the arc-shaped and spoke-wing groove seed disturbance structures shows that the spoke-wing groove structure increases the average seed speed by 17.0%, significantly improving the seed filling effect. A coupled Fluent-EDEM gas-solid simulation confirmed the optimal negative pressure to be 6.0 kPa, achieving a single-seed adsorption rate of 98.6%. Orthogonal experiments were performed to optimize the operating parameters, and the optimal combination with corresponding sowing performance indices was obtained. Field tests indicated that the performance of the planter meets the national standards for precision sowing when the operating speed is ≤12 km/h.

Existing methods often struggle to accurately and automatically separate livestock from complex background environments and are further hindered by severe noise interference in point cloud data, which leads to insufficient segmentation accuracy and ultimately affects the precision of livestock body measurements. To address these challenges, this study proposes a livestock body measurement method based on omnidirectional spatial localization segmentation. First, an omnidirectional point cloud segmentation model (Omni-PointMamba) was designed, which adopts an eight-directional scanning strategy in 3D space and an alternating dual-module architecture that integrates Point Mamba Blocks with convolutional modules. By enhancing spatial neighborhood modeling capabilities, the model efficiently fuses region-specific geometric features with comprehensive structural context, thereby achieving precise separation of livestock from background interference. Second, a spatial localization-based measurement method was developed. This method constructs a spatial coordinate system using ground normal vectors to achieve automatic rotation and alignment of the point cloud. It employs a two-step clustering strategy to identify key body parts, including the head and tail, and determines measurement landmarks through geometric point distribution analysis, enabling accurate, non-contact estimation of body size parameters. Experiments conducted on 20 pigs and 103 cattle demonstrate that the proposed model achieves outstanding segmentation performance, with mean Intersection over Union (mIoU) values of 0.9930 for pigs and 0.9685 for cattle. Furthermore, the model yields low mean absolute percentage errors (MAPE) in morphological measurements. For pigs, the MAPE for body width, hip width, and chest girth are 1.34%, 1.99%, and 1.37%, respectively. For cattle, the MAPE for body slant length, chest width, hip height, and heart girth are 2.24%, 3.28%, 2.18%, and 4.22%, respectively. These results indicate that the proposed method provides highly accurate segmentation and morphological measurement capabilities for both pigs and cattle.

Traditional ventilation control decisions for Chinese Solar Greenhouses (CSGs) rely primarily on farmers’ empirical judgment. With the development of a novel ventilation system (comprising bottom vents, top vents, and back roof vents), farmers lack adequate practical experience in operating such systems. To evaluate the cooling performance of the new ventilation system in CSGs, a Wireless Sensor Network (WSN) system was established using multiple wireless temperature and humidity sensors. The Ordinary Kriging (OK) interpolation method, which was developed in LabVIEW, was employed to visualize and monitor the real-time air temperature distribution under various ventilation opening configurations and vent combinations. Additionally, the cooling amplitude and temperature uniformity were comparatively analyzed. The results indicated that the synergy of multiple vents could enhance the chimney effect, achieving efficient cooling in summer. The cooling amplitudes of the dual-vent combination (bottom vent+top vent) and the three-vent configuration were 7.1°C and 10.4°C, respectively. Increasing the ventilation area improved both the cooling effect and temperature uniformity, with more open vents and a larger top vent area offering additional benefits. The findings of this study can provide a theoretical reference for farmers to optimize greenhouse ventilation management and offer data support for the application of novel ventilation systems.

Temperature is a critical factor influencing crop growth in controlled environment agriculture. Accurate regulation of air temperature within a greenhouse is essential for promoting optimal crop development and enhancing production efficiency. In this study, a Categorical Boosting model based on SMOTETomek mixed sampling method and improved Sparrow Search Algorithm (SMOTETomek-ISSA-CatBoost) was proposed to predict the categories of greenhouse temperature control modes. This study utilized historical temperature control mode data, which had been accumulated by cultivation experts through practical production and demonstrated effective in temperature management. To enhance the model’s performance and achieve real-time, precise temperature regulation in greenhouses, firstly, the SMOTETomek mixed sampling method was utilized to expand the original training set, effectively addressing the issue of data imbalance. Secondly, the Latin Hypercube Sampling (LHS) method, the Cauchy mutation perturbation operator, and a greedy rule were employed to refine the Sparrow Search Algorithm to enhance the global search capability. Ultimately, the improved Sparrow Search Algorithm was employed to optimize the hyper-parameters of CatBoost model to improve its predictive accuracy. Compared with SMOTETomek-CatBoost models optimized by Whale Optimization Algorithm (WOA), Fruit Fly Optimization Algorithm (FOA), Particle Swarm Optimization (PSO), and standard Sparrow Search Algorithm (SSA), the SMOTETomek-ISSA-CatBoost model demonstrated better prediction efficacy, with F1-score and AUC values reaching 0.8147 and 0.9629, respectively. The SMOTETomek-ISSA-CatBoost model exhibited the capability to predict the category of temperature control modes in a greenhouse accurately, thereby providing a decision-making foundation for intelligent management of greenhouse environments.

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.

Onions, often referred to as the “Queen of the kitchen”, play a vital role in enhancing dishes with their distinctive flavor, phenols, and flavonoids. Despite their significance, the onion market faces considerable volatility due to seasonal variations and substantial postharvest losses, resulting in significant price fluctuations. These losses can reach up to 40%, primarily due to improper handling, sprouting, rotting, inadequate storage, and limited infrastructure, with microbial spoilage alone accounting for 15%-20% of the total losses. Conventional onion storage methods demand substantial investments but often fail to effectively manage temperature and relative humidity (RH) fluctuations, which are critical for maintaining bulb shelf life. This study focuses on developing an on-farm evaporative cooled onion storage structure (ECOSS) to provide a long-term storage solution for onions. Results demonstrated that the ECOSS maintained internal temperatures between 24.5°C-31.3°C (average 27.1°C) and suitable RH levels throughout the storage period, with minimum values between 63.7%-67.2% and maximum values between 74.1%-75.5%. Physiological loss in weight (PLW), sprouting, and rotting were lowest at the 0.91 m storage height compared to 1.82 m after 5 months of storage. Onion firmness decreased over time, with the lowest firmness observed at 1.82 m (14.68 N). The net profit per ton of onions stored from May to September was INR6021 for the ECOSS, compared to INR2553 for traditional onion storage structure (TOSS). The ECOSS offers a cost-effective and efficient solution for reducing postharvest losses and enhancing onion storage.

To address the persistent challenges of low harvesting efficiency and seasonal labor shortages in Camellia oleifera production, this study proposes a novel canopy vibration harvester driven by a five-bar PRRRP parallel mechanism configured to generate an epitrochoid excitation trajectory. Through analysis of the epitrochoid excitation trajectory and in accordance with parallel mechanism design principles, the PRRRP configuration was selected, considering structural layout, transmission system, motion performance, and singularity distribution. On this basis, a vibration harvesting device driven by the PRRRP mechanism was designed and developed. Kinematic and dynamic analyses, as well as workspace modeling, were conducted, and real-time position acquisition of the driving components was achieved through inverse kinematics solutions. The harvesting device includes dual servo motor-driven linear modules, articulated linkages, and an adjustable excitation frame fitted with excitation rods to engage the tree canopy. Using Camellia oleifera cultivar of Changlin No. 40 as the target crop, the device generated an epitrochoid trajectory with a vibration frequency of 7 Hz and an amplitude of 90 mm, delivering multidirectional excitation to the canopy. By adjusting tree size parameters, the vibration response of branches with different inclination angles was investigated. The results showed that average vibration response accelerations for branches inclined at 0°-30°, 30°-60°, and 60°-90° were 14.04 m/s², 21.88 m/s², and 21.27 m/s², respectively, providing a theoretical basis for branch pruning. Harvesting trials indicated an overall fruit removal rate of 75.10% and an overall bud shedding rate of 11.55%, showing higher fruit removal and lower bud shedding than a traditional canopy vibration device employing linear reciprocating excitation. These results confirm that epitrochoid-based, parallel-mechanism excitation markedly improves fruit detachment efficiency and reduces collateral damage to tree architecture. Our study provides both theoretical insights and practical guidance for the development of next generation mechanized harvesters for Camellia oleifera and other woody fruit crops.

To address the challenges in labeling long labels on curved surface vegetables, such as wrinkles and label detachment, a cam-elliptical gear (C&E) labeling mechanism that realizes an improved hypocycloid trajectory is proposed. Firstly, the influence of different parameters on the hypocycloid trajectory is studied, and the tricuspid hypocycloid is selected as the labeling trajectory. Secondly, the trajectory and kinematic equations of the C&E labeling mechanism with a tricuspid hypocycloid trajectory are established. Next, the influence of various parameters on the trajectory and kinematics of the C&E labeling mechanism is examined. A set of optimal parameters is obtained through a comparative study, and a 3D model of the C&E labeling mechanism is established and simulated. Finally, a prototype of the C&E labeling mechanism was built and experimented with. The experiment showed that the normal labeling completion rate is 94%, and the prototype’s efficiency is 56.3 pcs/min. The research in this paper provides a theoretical basis for the design and optimization of a vegetable long-label curved-surface labeling mechanism.

To address high cleaning losses caused by high impurity content and uneven distribution during rapeseed threshing, this study proposes a drum pneumatic-assisted threshing and separation method. Initially, by determining the suspension velocity characteristics of various threshed components, auxiliary blades were designed and integrated to construct a non-uniform gradient airflow field, generating a directional pneumatic transport effect. CFD simulations confirmed that this configuration induces a stable axial spiral airflow. Driven dynamically by this airflow, miscellaneous materials are guided to migrate orderly toward the rear of the concave screen, enabling active source-based regulation of the spatial-temporal flow behavior and component distribution. Multi-parameter correlation analysis indicated a significant negative correlation (coefficient = –0.88) between axial airflow and the average impurity ratio. Field experiments demonstrated that at a feed rate of 2.8 kg/s with auxiliary blades, the grain loss rate was reduced to 3.7%, compared with 6.3% without auxiliary blades, corresponding to a 41.3% reduction. This research demonstrates that axial airflow can effectively regulate threshed material at the source, providing a novel approach for high-efficiency and low-loss threshing and cleaning.

Nitrous oxide (N₂O), a long-lived greenhouse gas, is primarily produced in agricultural soils through biological nitrification and denitrification processes. However, the effects of soil salinity on nitrogen transformation processes remain insufficiently understood, hindering the development of effective nitrogen management in salt-affected farmlands. In this study, laboratory incubation experiments combined with a ¹⁵N stable isotope tracing technique were conducted to quantify the effects of salt stress on nitrification and denitrification rates and their contributions to N₂O emissions. The results showed that during the first week of incubation, slight and moderate salinity (NaCl contents of 0.04% and 0.10%; EC_1:5≤1.10 dS/m) enhanced both nitrification and denitrification rates, whereas strong salinity (0.20% NaCl; EC_1:5≥1.42 dS/m) inhibited these processes. In the first week of incubation, nitrification and denitrification contributed approximately 65%-70% and 30%-35% to the total N₂O emissions under 60% water-filled pore space, respectively. These results indicate that nitrification represents the predominant source of N₂O production in saline soils during the first week following fertigation. The findings suggest that nitrogen management practices for inhibiting the nitrification process (e.g., the addition of nitrification inhibitors in companion with fertigation) may mitigate N₂O emissions in saline fields. This provides a scientific basis for optimizing nitrogen management in saline soils and reducing greenhouse gas emissions.

Current Lycium barbarum L. vibration harvesting equipment exhibits low levels of intelligence and precision, often resulting in a trade-off between efficiency and fruit damage. This study proposed a ripe fruit region detection model, YOLO-RFR, specifically for precision vibration harvesting of L. barbarum. First, the ADown downsampling module was introduced to replace part of the conventional convolution layers. Then, the C3k2-AP module, inspired by the asymmetric padding strategy, was designed to replace the C3k2 module. Additionally, the GCHead detection head was constructed using group convolution. Finally, the EMA-Slide Loss function was developed to optimize the classification performance by combining the slide weighting function with Exponential Moving Average (EMA). The experimental results showed that the model achieved precision, recall, and mAP of 93.7%, 92.0%, and 97.0%, respectively, representing improvements of 4.0%, 4.4%, and 2.6% over the baseline. The parameter, floating-point operations (FLOPs), and model size were 1.7 M, 4.1 G, and 3.8 MB, respectively, corresponding to decreases of 34.6%, 34.9%, and 30.9% compared with the baseline. To further validate its practical feasibility, the improved model was deployed on an NVIDIA Jetson AGX Xavier embedded device, achieving an inference speed of 163 fps with TensorRT acceleration. In conclusion, the YOLO-RFR model demonstrated excellent performance in detection accuracy, model lightweighting, and deployment on embedded devices, providing strong technical support for the precision vibration harvesting of L. barbarum.

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.

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.

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.

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.

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.

Yield monitoring is crucial for the agricultural sector, as it can be used to inform decisions on harvesting, storage, and transportation. Traditionally, several statistical methods and visual inspection techniques are employed to get an early estimate of the final yield of citrus, with the downside of being inaccurate, costly, and time-consuming. In recent years, there have been a lot of advancements in the fields of Artificial Intelligence (AI) and computer vision, providing opportunities to automate plenty of things in different domains, including agriculture. This research proposes a deep learning-based framework that leverages multiple Convolutional Neural Networks (CNN) to efficiently and effectively operate in real-world environments, using field data to provide accurate, improved yield estimates. A high-quality dataset, consisting of citrus tree images, is obtained from orchards at the university research farm Koont and the National Agriculture Research Center (NARC). Afterwards, the CNN-based models are trained thoroughly with various configurations and data augmentation techniques. All the models are rigorously tested and evaluated on the basis of a number of performance metrics. Experiments have shown that YOLOv8m performs with the highest mean average precision, reaching up to 90% with an inference time of a few milliseconds, making it worthy to be deployed for fruit detection, counting, and yield estimation tasks.

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.

Temporal upscaling of evapotranspiration (ET) is crucial for improving water use efficiency and water-saving irrigation management in crop production, playing a vital role in guiding farmland irrigation. This study investigated the water consumption patterns and evaluated different temporal upscaling methods for ET in drip irrigated grapevines in Northeast China’s cold region. Based on a three-year experiment (2018, 2020, 2021), four upscaling methods were examined: the evaporation fraction method (EF method), the improved evaporation fraction method (EF′ method), the crop coefficient method (K_c method), and the direct canopy resistance method (r_c method), applied to both instantaneous-to-daily and daily-to-whole-growth-period timescales. The results demonstrated that all key parameters, including evaporation fraction (EF), improved evaporation fraction (EF′), crop coefficient (K_c), and canopy resistance (r_c), exhibited stable values from 8:00-16:00 when upscaling ET from instantaneous to daily scales. Their mean standard deviations (SD) ranged from 0.08-0.21, 0.08-0.19, 0.10-0.18, and 41.43-137.72 s/m, respectively. Regarding the simulation of instantaneous to daily upscaling, the methods ranked as EF method>EF′ method>K_c method>r_c method. The EF method achieved optimal performance at specific times: 11:30 (shoot growth and the flowering period), 12:00 (fruit expansion), and 12:30 (maturity). For daily to whole growth period upscaling, all four methods performed best during the fruit expansion stage, maintaining the same performance ranking. The EF method consistently exhibited the smallest errors, with MAE values of 35.31 mm, 33.00 mm, and 42.97 mm, and RRMSE values of 12.21%, 11.40%, and 14.62% in 2018, 2020, and 2021, respectively. Therefore, the EF method is recommended for both upscaling ET from instantaneous to daily timescales and from daily to the entire growth period for drip-irrigated grapevines in Northeast China’s cold region. The findings not only enrich the analytical framework for agricultural hydrological processes, but also hold significant implications for implementing “hourly precision water management” in greenhouse grape cultivation and enhancing water use efficiency in greenhouse grape systems.

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.

Olive leaves and pomace are being wasted in Pakistan and remain unexplored concerning their bioactivity and fatty acids. The objective of the present work is to study fatty acids and antioxidant potentials of olive leaves and fruit pomace indigenous to Pakistan. The antioxidant potential of both water extract (WE) and ethanol extract (EE) of olive leaves and fruit pomace was also investigated. Results showed that olive leaves exhibited the highest oleic acid content (54.60%±3.40%), followed by palmitic acid (15.20%±1.20%). For olive pomace, oleic acid was the most abundant fatty acid (59.30%±2.40%), while palmitic acid and linoleic acid were recorded at 6.30%±0.56% and 5.2%±0.61%, respectively. However, oleic acid (59.30%±2.40%) was present in the highest quantities in olive pomace, followed by palmitic acid (6.30%±0.56%) and linoleic acid (5.20%±0.61%). The EE of olive leaves had higher total phenolics (1902.00±189.00 mg GAE/100 g) than WE (1216.00±143.00 mg GAE/100 g). The WE of olive pomace had higher total phenolics (292.40±18.00 mg GAE/100 g) than EE (201.80±15.00 mg GAE/100 g). The scavenging activity of DPPH of ethanol-soluble extracts (EE) (60.60±3.50 µM TE/g) of olive leaves was also greater than their respective water-soluble extracts (WE) (51.20±3.00 µM TE/g). Similarly, scavenging activity of ABTS of EE (9.88±0.90 µM TE/g) of olive leaves was greater as compared to WE (3.24±0.40 µM TE/g) of leaves. The DPPH radical scavenging activity of EE (29.44±1.10 µM TE/g) of olive pomace was higher than its respective WE (14.24±1.40 µM TE/g). However, the ABTS scavenging activity of EE (6.05±0.90 µM TE/g) of olive pomace did not vary significantly (p<0.05) with regard to its respective WE (5.99±0.45 µM TE/g). Results concluded that olive leaves and olive pomace are good sources of fatty acids and antioxidants, which are recommended in food application according to a sustainable approach strategy.