Search Results (1,789)

Weeds pose a significant threat to crop yields, both in quantitative and qualitative terms. Modern agriculture relies heavily on herbicides; however, their excessive use can lead to negative environmental impacts. As a result, recent research has increasingly focused on Integrated Weed Management (IWM), which employs multiple complementary strategies to control weeds in a holistic manner. Nevertheless, large-scale adoption of this approach requires a solid understanding of the underlying tactics. This systematic review analyses recent studies (2013–2022) on herbicide alternatives for weed control across major cropping systems in the EU-27 and the UK, providing an overview of current knowledge, the extent to which IWM tactics have been investigated, and the main gaps that help define future research priorities. The review relied on the IWMPRAISE framework, which classifies weed control tactics into five pillars (direct control, field and soil management, cultivar choice and crop establishment, diverse cropping systems, and monitoring and evaluation) and used Scopus as a scientific database. The search yielded a total of 666 entries, and the most represented pillars were Direct Control (193), Diverse Cropping System (183), and Field and Soil Management (172). The type of crop most frequently studied was arable crops (450), and the macro-area where the studies were mostly conducted was Southern Europe (268). The tactics with the highest number of entries were Tillage Type and Cultivation Depth (110), Cover Crops (82), and Biological Control (72), while those with the lowest numbers were Seed Vigor (2) and Sowing Depth (2). Overall, this review identifies research gaps and sets priorities to boost IWM adoption, leading policy and funding to expand sustainable weed management across Europe. Full article

Path tracking is a crucial part of agricultural machinery automatic navigation system (ANS) and has been extensively investigated in prior research. Although existing ANS designs perform satisfactorily under mild soil condition, path-tracking algorithms are often challenged by unknown disturbances arising from complicated field environment and machine conditions. The current literature lacks a detailed analysis of the influence of the sideslip angle under specific operating speeds and path scenarios for agricultural machinery, which serves as the primary motivation for this study. In this paper, simulations are conducted for sprayers and harvesters across various paths, curvatures, and speeds to analyze the impact of sideslip on path-tracking performance. The results indicate that under the typical low-speed and large-curvature conditions of agricultural machinery, neglecting sideslip effects leads to a mismatch between the theoretical model and the actual vehicle motion. Compared to an MPC based on a kinematic model that disregards the sideslip angle, explicitly incorporating the sideslip angle into the kinematic model reduces the maximum lateral tracking error from 0.234 m to 0.174 m for a U-shaped path, and from 0.263 m to 0.194 m for a rectangular-shaped path. Simulation at different travel speeds further demonstrates that proposed algorithm achieves smaller sideslip amplitudes and faster attenuation after completing turns compared to conventional MPC. These findings offer valuable insights for the design of path-tracking algorithms in agricultural machinery autonomous driving systems. Full article

With the rapid development of unmanned agricultural machinery technology, the accuracy and stability of agricultural machinery path tracking have become key challenges in achieving precision agriculture. To address the issues of insufficient accuracy and stability in path tracking for rice transplanters in paddy fields, this study proposes a composite control strategy that integrates the extended line-of-sight (LOS) guidance law with an adaptive fuzzy sliding mode control law. By establishing a two degree of freedom dynamic model of the rice transplanter, two extended state observers are designed to estimate the longitudinal and lateral velocities of the rice transplanter in real time. A dynamic compensation mechanism for the sideslip angle is introduced, significantly enhancing the adaptability of the traditional look-ahead guidance law to soil slippage. Furthermore, by combining the approximation capability of fuzzy systems with the adaptive adjustment method of sliding mode control gains, a front wheel steering control law is designed to suppress complex environmental disturbances. The global stability of the closed-loop system is rigorously verified using the Lyapunov theory. Simulation results show that compared to the traditional Stanley algorithm, the proposed method reduces the maximum lateral error by 38.3%, shortens the online time by 23.9%, and decreases the steady-state error by 15.5% in straight-line path tracking. In curved path tracking, the lateral and heading steady-state errors are reduced by 19.2% and 14.6%, respectively. Field experiments validate the effectiveness of this method in paddy fields, with the absolute lateral error stably controlled within 0.1 m, an average error of 0.04 m, and a variance of 0.0027 m². Full article

Hilly and mountainous areas are important agricultural production regions globally. Their dramatic topography, dense fruit tree planting, and steep slopes severely restrict the application of traditional plant protection machinery. Pest and disease control has long relied on manual spraying, resulting in high labor intensity, low efficiency, and pesticide utilization rates of less than 30%. Plant protection UAVs, with their advantages of flexibility, high efficiency, and precise application, provide a feasible technical approach for plant protection operations in hilly and mountainous areas. However, steep slopes and dense orchard environments place higher demands on key technologies such as drone positioning and navigation, attitude control, trajectory planning, and terrain following. Achieving accurate identification and adaptive following of the undulating fruit tree canopy while maintaining a constant spraying distance to ensure uniform pesticide coverage has become a core technological bottleneck. This paper systematically reviews the key technologies and research progress of plant protection UAVs in hilly and mountainous operations, focusing on the principles, advantages, and limitations of core methods such as multi-sensor fusion positioning, intelligent SLAM navigation, nonlinear attitude control and intelligent control, three-dimensional trajectory planning, and multimodal terrain following. It also discusses the challenges currently faced by these technologies in practical applications. Finally, this paper discusses and envisions the future of plant protection UAVs in achieving intelligent, collaborative, and precise operations on steep slopes and in dense orchards, providing theoretical reference and technical support for promoting the mechanization and intelligentization of mountain agriculture. Full article

Crop root development, and in turn crop growth, is strongly influenced by soil strength and the mechanical impedance of compacted layers, which restrict root elongation and exploration. Because the depth and thickness of compacted layers vary across a field, their identification is essential for site-specific tillage and sustainable root-zone management. A sensing approach that can support future real-time identification of compacted layers after soil-specific calibration, which would enable variable-depth tillage, reducing mechanical impedance and improving energy-use efficiency while maintaining crop yields. This study aimed to develop and evaluate prediction models that can support future real-time identification of compacted soil layers using soil electrical conductivity (EC) and moisture content as non-destructive indicators. A sandy clay soil (48.6% sand, 29.3% clay, 22.1% silt) was tested in a soil-bin laboratory under controlled conditions at three moisture levels (13, 18, and 22% db.) and six depth layers (C1–C6, 0–30 cm) identified from the penetration-resistance profile to measure penetration resistance, shear resistance, and EC. Penetration and shear resistance increased toward the most resistant depth layer and decreased with increasing moisture content, whereas EC generally increased with both depth layer and moisture content. Linear regression models relating penetration resistance ($R^2=0.893$) and shear resistance ($R^2=0.782$) to EC and moisture content were developed and evaluated. Field validation in a paddy field of similar texture showed that predicted penetration resistance differed from measured values by 3–6% across the three compaction treatments evaluated. Root length density and root volume decreased with increasing machine-induced compaction, confirming the agronomic relevance of the modeled patterns and supporting the suitability of the proposed indicators. Together, these results demonstrate that EC and moisture content can potentially be used as non-destructive proxies for compacted-layer identification and provide a calibration basis for future on-the-go sensing systems to support site-specific, variable-depth tillage in agricultural fields. Full article

To address the pronounced issue of noise and vibration within the combine harvester cab, this study proposes a hybrid simulation and experimental validation approach that integrates the pivot noise transfer function (NTF) with a finite element method (FEM)-based vibroacoustic coupling analysis. A coupled finite element model combining the cab structure and its internal acoustic cavity was developed, with the excitation path characteristics explicitly defined. The coupled interaction between structural and acoustic modes, along with its influence on noise transmission, was systematically examined. The analysis revealed a significant transmission peak near 18 Hz at critical pivot Point D under specific excitation directions, indicating strong directional sensitivity in the excitation–response relationship. Experimental validation showed that the discrepancy between simulated and measured responses, including the NTFs, remained within 15%, confirming the accuracy and applicability of the proposed method. This research offers a reliable analytical framework and practical reference for noise and vibration reduction in agricultural machinery cab design. Full article

Silica-rich dust intrusion is a persistent challenge for lubrication systems in agricultural machinery, where abrasive third-body particles can accelerate wear and shorten component service life. Here, molecular dynamics simulations are employed to elucidate how SiO₂ nanoparticle contamination degrades polyalphaolefin (PAO) boundary lubrication at the atomic scale. Two confined sliding models are compared: a pure PAO film and a contaminated PAO film containing 7 wt% SiO₂ nanoparticles between crystalline Fe substrates under a constant normal load and sliding velocity. The contaminated system exhibits a higher steady-state friction force, faster lubricant film disruption and migration, and consistently higher interfacial temperatures, indicating intensified energy dissipation. Substrate analyses reveal deeper and stronger von Mises stress penetration, increased severe plastic shear strain, elevated Fe potential energy associated with defect accumulation, and reduced structural order. Meanwhile, PAO molecules store more intramolecular deformation energy (bond, angle, and dihedral terms), reflecting stress concentration and disturbed shear alignment induced by nanoparticles. These results clarify the multi-pathway mechanisms by which abrasive SiO₂ contaminants transform PAO from a protective boundary film into an agent promoting abrasive wear, providing insights for designing wear-resistant lubricants and improved filtration strategies for particle-laden applications. Full article

In response to the growing need for efficient, stable, and non-destructive gripping in apple harvesting robots, this study proposes a novel rigid–flexible coupled end-effector. The design integrates an underactuated mechanism with a real-time force feedback control system. First, compression tests on ‘Red Fuji’ apples determined the minimum damage threshold to be 24.33 N. A genetic algorithm (GA) was employed to optimize the geometric parameters of the finger mechanism for uniform force distribution. Subsequently, a rigid–flexible coupled multibody dynamics model was established to simulate the grasping of small (70 mm), medium (80 mm), and large (90 mm) apples. Additionally, a harvesting experimental platform was constructed to verify the performance. Results demonstrated that by limiting the contact force of the distal phalange region silicone (DPRS) to 24 N via active feedback, the peak contact forces on the proximal phalange region silicone (PPRS) and middle phalange region silicone (MPRS) were effectively maintained below the damage threshold across all three sizes. The maximum equivalent stress remained significantly below the fruit’s yield limit, ensuring no mechanical damage occurred, with an average enveloping time of approximately 1.30 s. The experimental data showed strong agreement with the simulation, with a mean absolute percentage error (MAPE) of 5.98% for contact force and 5.40% for enveloping time. These results confirm that the proposed end-effector successfully achieves high adaptability and reliability in non-destructive harvesting, offering a valuable reference for agricultural robotics. Full article

The hard bottom layer in paddy fields significantly impacts the driving stability, operational quality, and efficiency of agricultural machinery. Continuously improving the precision and efficiency of unmanned, precision operations for paddy field machinery is essential for realizing unmanned smart rice farms. Addressing the unclear influence patterns of hard bottom contours on typical scenarios of agricultural machinery motion and posture changes, this paper employs a rice transplanter chassis equipped with GNSS and AHRS. It proposes methods for acquiring motion state information and hard bottom contour data during agricultural operations, establishing motion state expression models for key points on the machinery antenna, bottom of the wheel, and rear axle center. A correlation analysis method between motion state and hard bottom contour parameters was established, revealing the influence mechanisms of typical hard bottom contours on machinery trajectory deviation, attitude response, and wheel trapping. Results indicate that hard bottom contour height and local roughness exert extremely significant effects on agricultural machinery heading deviation and lateral movement. Heading variation positively correlates with ridge height and negatively with wheel diameter. The constructed mathematical model for heading variation based on hard bottom contour height difference and wheel diameter achieves a coefficient of determination R² of 0.92. The roll attitude variation in agricultural machinery is primarily influenced by the terrain characteristics encountered by rear wheels. A theoretical model was developed for the offset displacement of the antenna position relative to the horizontal plane during roll motion. The accuracy of lateral deviation detection using the posture-corrected rear axle center and bottom of the wheel center improved by 40.7% and 39.0%, respectively, compared to direct measurement using the positioning antenna. During typical vehicle-trapping events, a segmented discrimination function for trapping states is developed when the terrain profile steeply declines within 5 s and roughness increases from 0.008 to 0.012. This method for analyzing how hard bottom terrain contours affect the position and attitude changes in agricultural machinery provides theoretical foundations and technical support for designing wheeled agricultural robots, path-tracking control for unmanned precision operations, and vehicle-trapping early warning systems. It holds significant importance for enhancing the intelligence and operational efficiency of paddy field machinery. Full article

In response to the challenges posed by high operational resistance and significant soil adhesion faced by traditional pressing rollers in moist clay soils, this study introduces a bionic pressing roller inspired by the imbricated scale structure of the pangolin. The fundamental working principles of the roller are elucidated, and its key structural parameters are designed. Utilizing the discrete element method (DEM), the structural parameters of the bionic scales are optimized through Response Surface Methodology (RSM), with traveling resistance and the mass of adhered soil serving as evaluation indicators. Field experiments are conducted to validate the operational performance of the bionic roller. The optimal parameter combination is identified as follows: a scale length of 130 mm, 10 scales, and an overlap rate of 50%. Field comparison tests reveal that the bionic roller significantly reduces traveling resistance by 11.0% and decreases the mass of adhered soil by 47.2% compared to the traditional roller at a soil moisture content of 35%. This study confirms that the bionic roller, which mimics the pangolin scale structure, demonstrates superior anti-adhesion and drag-reduction characteristics. The findings are anticipated to provide a reference for the energy-efficient design of soil-engaging components in agricultural machinery, including ridging and shaping machines. Full article

The unique geo-ecological conditions of karst river basins (KRBs) heighten rural vulnerability to compound disturbances; therefore, enhanced rural resilience (RR) is critical for regional ecological security and sustainable development. In this study, the Wujiang River Basin was chosen as the study area. A comprehensive evaluation index system was first established to assess RR. Key driving factors were identified using the Optimal Parameters-based Geographical Detector (OPGD) mode. The Geographically and Temporally Weighted Regression (GTWR) model was then applied to analyze the spatiotemporal heterogeneity in the driving mechanisms of RR. Our results show that from 2010 to 2022: (1) RR in the study area increased significantly, and disparities among counties decreased notably, indicating a trend toward more balanced regional development. (2) RR displayed strong positive spatial autocorrelation, with spatial clusters evolving dynamically under the influence of policy interventions and environmental constraints. (3) The main drivers of spatial heterogeneity in RR included urban–rural income disparity, road network density, agricultural machinery power, etc. Their driving mechanisms exhibited significant spatiotemporal non-stationarity. The findings inform the development of targeted strategies to enhance regional resilience. Additionally, the methodology and empirical insights can serve as valuable references for RR research and practice in other similar KRBs worldwide. Full article

Due to the continuous improvement in the usage area and retention quality of plastic films in China, the serious residue film pollution faced by China has become a major threat to crop production. To address the aforementioned issues and in accordance with the actual demand for residue film recovery machines in the Xinjiang region of China, a residual film impurity separation device suitable for the recovery machine of crop residue films has been designed. The overall structure and working principle of the machine were elaborated. Numerical simulations of the through-flow fan device of the residual film recovery machine were carried out using the ANSYS 2022 (CFX) finite element analysis platform, and the corresponding wind speed range of the fan at rotational speeds of 1000–1400 r/min was obtained. Based on the simulation results, the Depth of Machine Insertion into the Ground, Fan Wind Speed, and Forward Speed of the Machinery were selected as experimental factors, while the residual film recovery rate was taken as the evaluation index. A response surface experiment was conducted, and the optimization analysis was performed using Design-Expert software. The final experimental validation results indicated that when the Depth of Machine Insertion into the Ground was 32 mm, the Forward Speed of the Machinery was 5.29 km/h, and the Fan Wind Speed was 13.67 m/s, the machine could effectively overcome the influence of complex field operating conditions. This parameter combination was identified as the optimal operating condition of the machine, providing a valuable reference for the design and optimization of related agricultural machinery. Full article

Scheduling agricultural machinery across multiple cooperatives is often inefficient because existing rigid, discrete assignment models fail to flexibly coordinate shared resources under tight time windows. To address this limitation, we develop a simulation-based framework for the Multi-cooperative Agricultural Machinery Scheduling Problem (MAMSP) underpinned by a Continuous Collaborative Workload Sharing (CWS) formulation. To mitigate the solution fragmentation inherent in continuous optimization, we propose a Hybrid Particle Swarm Optimization with Sparsity Repair (HPSO-SR). The algorithm integrates a stochastic initialization strategy to enhance global exploration, a mutation injection mechanism to avoid swarm stagnation, and a sparsity repair operator that prunes uneconomical fractional assignments, yielding operationally feasible sparse schedules. A real-world case study from Liyang, China, augmented by synthetic instances of varying scales (small, medium, and large), was conducted to benchmark the proposed approach against a rule-based heuristic, a Genetic Algorithm (GA-CWS), and Simulated Annealing (SA-CWS) under a unified decoding scheme. The results show that HPSO-SR consistently achieves the lowest objective values, reducing the total cost by 74.43% relative to GA-CWS and 59.20% relative to SA-CWS in the medium-scale case. By deliberately trading off minimal additional transfer cost against improved timeliness, the obtained schedules nearly eliminate delay penalties. Sensitivity analysis and mechanism ablation studies further confirm that the sparse solutions exhibit structural resilience and that the proposed repair strategy is essential for algorithmic convergence, supporting the reliability of the proposed approach for time-critical, high-stakes agricultural operations. Full article

Microalgae are an evolutionarily ancient and morphologically diverse group of photosynthetic eukaryotes, with taxonomic resolution complicated by environmentally driven phenotypic plasticity. This study merges deep learning and explainable artificial intelligence (XAI) to establish a transparent, reliable, and biologically meaningful framework for green microalgae (Chlorophyta) classification. Microscope images from three morphologically distinct algal species—Desmodesmus flavescens, Desmodesmus subspicatus, and Tetradesmus dimorphus representing the genera Desmodesmus and Tetradesmus within Chlorophyta—were analyzed using twelve convolutional neural networks, including EfficientNet-B0–B7, DenseNet201, NASNetLarge, Xception, and ResNet152V2. A curated dataset comprising 3624 microscopic images from three Chlorophyta species was used, split into training, validation, and test subsets. All models were trained using standardized preprocessing and data augmentation procedures, including grayscale conversion, CLAHE-based contrast enhancement, rotation, flipping, and brightness normalization. The model’s performance was assessed using accuracy and loss metrics on independent test datasets, while interpretability was evaluated through saliency maps and Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations. ResNet152V2 achieved the highest overall performance among all evaluated architectures, outperforming EfficientNet variants, NASNetLarge, and Xception in terms of macro F1-score. Visualization analysis showed that both Grad-CAM and saliency mapping consistently highlighted biologically relevant regions—including cell walls, surface ornamentation, and colony structures—confirming that the models relied on taxonomically meaningful features rather than background artifacts. The findings indicate that the integration of deep learning and XAI can attain consistently high test accuracy for microalgal species, even with constrained datasets. This approach enables automated taxonomy and supports biodiversity monitoring, ecological assessment, biomass optimization, and biodiesel production by integrating interpretability with high predictive accuracy. Full article

This article presents an in-depth analysis of the application of thermal deposition techniques, in particular thermal spraying, to improve the properties of materials used in agricultural components that work the soil, such as agricultural plows (mainshare and foreshare). Due to the difficult operating conditions, characterized by abrasive wear, mechanical shocks, and chemical exposure from various soils, these surface coatings aim to increase the durability and corrosion resistance of the materials of components intended for working with the soil. The study investigates thermal deposition methods and their effects on the microstructure, hardness, and friction resistance of the obtained layers. The study highlights experiments that reveal significant improvements in mechanical properties, highlighting superior behavior in real conditions of agricultural use. Nevertheless, soil types significantly influence the abrasive wear rate of the components and also their corrosion, which depends on the soil pH. The results confirm that the use of thermal deposition represents a sustainable and effective solution for extending the life of plows, thus reducing maintenance costs and increasing the efficiency of agricultural processes. This research contributes to the optimization of agricultural equipment, providing an innovative approach for adapting plows to the increasing demands of agricultural exploitation. Full article