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.

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.

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.

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.

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.

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.

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.

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.

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.