Search Results (50)

Continuous cropping obstacles (CCOs) lead to a decline in yield and quality under repeated cultivation in the same farmland. Notably, CCOs caused by fusarium wilt, autotoxicity, or imbalance in rhizosphere microbial communities reduce the productivity of watermelons (Citrullus lanatus). Considering the negative environmental impacts of conventional agrochemicals, it is necessary to evaluate the biocontrol efficiency of microorganisms. Therefore, this study aimed to investigate the biocontrol efficiency of Bacillus amyloliquefaciens strain PP19 against CCOs of watermelon so as to develop alternatives to agrochemicals. The inhibitory effect of PP19 on watermelon fusarium wilt was assessed through plate confrontation assays and field trials. The degradation and utilization of autotoxins by PP19 were examined via co-culture experiments. Additionally, 16S rRNA sequencing was employed to analyze the impact of PP19 on the rhizosphere soil microbial community of watermelon. Specifically, we analyzed the PP19 utilization of four phenolic autotoxins secreted by watermelon roots and assessed their effects on microbial diversity in the watermelon rhizosphere. Plant growth assays showed that PP19 improved the weight and quality of watermelon fruit. Although PP19 inhibited the growth of Fusarium oxysporum f. sp. niveum (Fon), the growth inhibitory effect was significantly enhanced by autotoxins produced by watermelon, including mixed phenolic, cinnamic, ferulic, and p-coumaric acids. Additionally, PP19 effectively degraded and utilized the autotoxins, and the autotoxins enhanced PP19’s swimming ability and biofilm formation. Moreover, PP19 treatment significantly enhanced the microbial diversity in watermelon rhizosphere, increased the number of beneficial bacterial genera, and decreased the number of pathogenic genera. Conclusively, these results suggest that B. amyloliquefaciens strain PP19 improves the resistance of watermelon to CCOs by effectively utilizing and degrading autotoxin, altering soil microbial community structure, and inhibiting Fon17 growth, resulting in improved fruit quality. Overall, PP19 possesses potential application as a biological control agent against CCOs in commercial watermelon cultivation. Full article

To address the operational demands of irregular farmland with fixed obstacles, this study proposes a full-coverage path planning framework that integrates UAV-based 3D perception and angle-adaptive optimization. First, digital orthophoto maps (DOMs) and digital elevation models (DEMs) were reconstructed from low-altitude aerial imagery. The feasible working region was constructed by shrinking field boundaries inward and dilating obstacle boundaries outward. This ensured sufficient safety margins for machinery operation. Next, segmentation angles were scanned from 0° to 180° to minimize the number and irregularity of sub-regions; then a two-level simulation search was performed over 0° to 360° to optimize the working direction for each sub-region. For each sub-region, the optimal working direction was selected based on four criteria: the number of turns, travel distance, coverage redundancy, and planning time. Between sub-regions, a closed-loop interconnection path was generated using eight-directional A* search combined with polyline simplification, arc fitting, Chaikin subdivision, and B-spline smoothing. Simulation results showed that a 78° segmentation yielded four regular sub-regions, achieving 99.97% coverage while reducing the number of turns, travel distance, and planning time by up to 70.42%, 23.17%, and 85.6%. This framework accounts for field heterogeneity and turning radius constraints, effectively mitigating path redundancy in conventional fixed-angle methods. This framework enables general deployment in agricultural field operations and facilitates extensions toward collaborative and energy-optimized task planning. Full article

The scenario adaptability of agricultural machinery chassis in hilly and mountainous regions has become a key area of innovation in modern agricultural equipment development in China. Due to the fragmented nature of farmland, steep terrain (often exceeding 15°), complex topography, and limited suitability for mechanization, traditional agricultural machinery experiences significantly reduced operational efficiency—typically by 30% to 50%—along with poor mobility. These limitations impose serious constraints on grain yield stability and the advancement of agricultural modernization. Therefore, enhancing the scenario-adaptive performance of chassis systems (e.g., slope adaptability ≥ 25°, lateral tilt stability > 30°) is a major research priority for China’s agricultural equipment industry. This paper presents a systematic review of the global development status of agricultural machinery chassis tailored for hilly and mountainous environments. It focuses on three core subsystems—power systems, traveling systems, and leveling systems—and analyzes their technical characteristics, working principles, and scenario-specific adaptability. In alignment with China’s “Dual Carbon” strategy and the unique operational requirements of hilly–mountainous areas (such as high gradients, uneven terrain, and small field sizes), this study proposes three key technological directions for the development of intelligent agricultural machinery chassis: (1) Multi-mode traveling mechanism design: Aimed at improving terrain traversability (ground clearance ≥400 mm, obstacle-crossing height ≥ 250 mm) and traction stability (slip ratio < 15%) across diverse landscapes. (2) Coordinated control algorithm optimization: Designed to ensure stable torque output (fluctuation rate < ±10%) and maintain gradient operation efficiency (e.g., less than 15% efficiency loss on 25° slopes) through power–drive synergy while also optimizing energy management strategies. (3) Intelligent perception system integration: Facilitating high-precision adaptive leveling (accuracy ± 0.5°, response time < 3 s) and enabling terrain-adaptive mechanism optimization to enhance platform stability and operational safety. By establishing these performance benchmarks and focusing on critical technical priorities—including terrain-adaptive mechanism upgrades, energy-drive coordination, and precision leveling—this study provides a clear roadmap for the development of modular and intelligent chassis systems specifically designed for China’s hilly and mountainous regions, thereby addressing current bottlenecks in agricultural mechanization. Full article

Sustainable cropland management is essential in maintaining national food security. In the black soil regions of China, which are key areas for commercial grain production, sustainable land use must be achieved urgently. To address the absence of integrated, large-scale, remote sensing-based sustainability frameworks in China’s black soil zones, we developed a comprehensive evaluation system with 13 indicators from four dimensions: the soil capacity, the natural capacity, the management level, and crop productivity. With this system and the entropy weight method, we systematically analyzed the spatiotemporal patterns of cropland sustainability in the selected black soil regions from 2010 to 2020. Additionally, a diagnostic model was applied to identify the key limiting factors constraining improvements in cropland sustainability. The results revealed that cropland sustainability in Heilongjiang Province has increased by 7% over the past decade, largely in the central and northeastern regions of the study area, with notable gains in soil capacity (+15.6%), crop productivity (+22.4%), and the management level (+4.8%). While the natural geographical characteristics show no obvious improvement in the overall score, they display significant spatial heterogeneity (with better conditions in the central/eastern regions than in the west). Sustainability increased the most in sloping dry farmland and paddy fields, followed by plain dry farmland and arid windy farmland areas. The soil organic carbon content and effective irrigation amount were the main obstacles affecting improvements in cropland sustainability in black soil regions. Promoting the implementation of technical models, strengthening investment in cropland infrastructure, and enhancing farmer engagement in black soil conservation are essential in ensuring long-term cropland sustainability. These findings provide a solid foundation for sustainable agricultural development, contributing to global food security and aligning with SDG 2 (zero hunger). Full article

By constructing a comprehensive evaluation index system for the development level of Agricultural Machinery Socialization Services (AMSS) in China’s hilly and mountainous areas, the article adopts the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) entropy weight method to carry out a comprehensive evaluation of the development level of AMSS in China’s 17 major hilly mountainous provinces, and utilizes the obstacle degree model and the Dagum Gini coefficient decomposition method to deeply explore the developmental constraints and regional differences in characteristics. The results of the study show that the development level of AMSS in all provinces is generally on the rise, and the overall development level of the Southwest region is relatively lagging behind, with significant differences from other regions. The obstacle degree model shows that industrial development, Government funding, and farmland construction are the main factors constraining AMSS in hilly and mountainous areas, specifically, the degree of coverage of AMSS, the percentage of agricultural machinery professional cooperatives, the degree of land fragmentation, and the level of agricultural machinery extension inputs have a greater impact on the level of development of AMSS. Dagum Gini coefficient calculations show that the overall relative differences in development levels have a tendency to decrease, but the level of development of agricultural machinery socialization in the southwestern hilly and mountainous second-maturity areas is still low, with an imbalance in development within the region and a more significant gap with the development levels of other hilly and mountainous regions. Full article

Tennessee’s ecological diversity, spanning forests, farmland, and urban areas, provides an ideal foundation for applying the One Health approach, which integrates human, animal, and environmental health. This review examines Tennessee’s current One Health landscape, highlighting active initiatives, ongoing challenges, and future directions. Key efforts involve workforce development, disease surveillance, outbreak response, environmental conservation, and public education, led by a coalition of state agencies, universities, and the Tennessee One Health Committee. These programs promote cross-sector collaboration to address issues such as zoonotic diseases, climate change, land use shifts, and environmental contaminants. Notably, climate-driven changes, including rising temperatures and altered species distributions, pose increasing threats to health and ecological stability. Tennessee has responded with targeted monitoring programs and climate partnerships. Education is also a priority, with the growing integration of One Health into K–12 and higher education to build a transdisciplinary workforce. However, the state faces barriers, including limited funding for the One Health workforce, undefined workforce roles, and informal inter-agency data sharing. Despite these obstacles, Tennessee’s successful responses to outbreaks like avian influenza and rabies demonstrate the power of coordinated action. To strengthen its One Health strategy, the state must expand funding, formalize roles, improve data systems, and enhance biodiversity and climate resilience efforts positioning itself as a national leader in interdisciplinary collaborative solutions. Full article

Unmanned agricultural machinery can significantly reduce labor intensity while substantially enhancing operational efficiency and production benefits. However, the presence of various obstacles in complex farmland environments is inevitable. Accurate and efficient obstacle recognition technology, along with a reliable safety warning system, is a crucial prerequisite for ensuring the safe and stable operation of unmanned agricultural machinery. This study proposes a lightweight model for farmland obstacle detection by improving the YOLOv8n object detection algorithm. Specifically, we introduce the Context-Guided Block (CG Block) in the C2f module and the Context-Guide Fusion Module (CGFM) in the Feature Pyramid Network (FPN) to enhance the model’s contextual information perception during feature extraction and fusion. Additionally, we employ a Lightweight Shared Convolutional Separable Batch Normalization Detection Head in the detection head, which significantly reduces the number of parameters while improving detection accuracy. Experimental results demonstrate that our method achieves a mean average precision (mAP) of 92.3% at 59.1 frames per second (FPS). The improved model reduces parameter count and computational complexity by 31.9% and 33.4%, respectively, with a model size of only 4.2 MB. Compared to other algorithms, the proposed model maintains an optimal balance between parameter efficiency, computational cost, detection speed, and accuracy, exhibiting distinct advantages. Furthermore, we propose a safety warning strategy based on the relative velocity and distance between obstacles and the unmanned agricultural machinery. Field experiments conducted under this strategy reveal an overall warning accuracy of up to 86%, verifying the reliability of the safety warning system. This ensures that unmanned agricultural machinery can effectively mitigate potential safety risks during field operations. Full article

Agriculture remains a key source of greenhouse gas (GHG) emissions within the European Union, posing substantial obstacles to achieving climate objectives and fostering sustainable development. On this background, organic farming stands out as a viable alternative, offering significant potential for reducing emissions. This study explores the impact of expanding organic farming on GHG emissions in the EU agricultural sector. The empirical research examines the connection between organic farming practices and GHG emission levels using structural equation modeling, complemented by Holt and ARIMA forecasting models, to project future trends based on expected growth in organic farmland. The findings highlight a robust negative influence (p < 0.001), demonstrating that organic farming practices are associated with tangible reductions in emissions. Forecasting analyses further reinforce this, predicting considerable declines in GHG emissions (by almost 14 percent below the level of 2008) as organic farming continues to expand for over 23% of agricultural land by 2035, according to the projections in this research. These insights underscore the critical role of organic farming in advancing the EU’s climate ambitions. The study concludes that broader adoption of organic practices offers a practical and impactful pathway for building a more sustainable agricultural system while mitigating environmental harm across member states. Full article

To satisfy the obstacle avoidance requirements of unmanned agricultural machinery during autonomous operation and address the challenge of rapid obstacle detection in complex field environments, an improved field obstacle detection model based on YOLOv8 was proposed. This model enabled the fast detection and recognition of obstacles such as people, tractors, and electric power pylons in the field. This detection model was built upon the YOLOv8 architecture with three main improvements. First, to adapt to different tasks and complex environments in the field, improve the sensitivity of the detector to various target sizes and positions, and enhance detection accuracy, the CBAM (Convolutional Block Attention Module) was integrated into the backbone layer of the benchmark model. Secondly, a BiFPN (Bi-directional Feature Pyramid Network) architecture took the place of the original PANet to enhance the fusion of features across multiple scales, thereby increasing the model’s capacity to distinguish between the background and obstacles. Third, WIoU v3 (Wise Intersection over Union v3) optimized the target boundary loss function, assigning greater focus to medium-quality anchor boxes and enhancing the detector’s overall performance. A dataset comprising 5963 images of people, electric power pylons, telegraph poles, tractors, and harvesters in a farmland environment was constructed. The training set comprised 4771 images, while the validation and test sets each consisted of 596 images. The results from the experiments indicated that the enhanced model attained precision, recall, and average precision scores of 85.5%, 75.1%, and 82.5%, respectively, on the custom dataset. This reflected increases of 1.3, 1.2, and 1.9 percentage points when compared to the baseline YOLOv8 model. Furthermore, the model reached 52 detection frames per second, thereby significantly enhancing the detection performance for common obstacles in the field. The model enhanced by the previously mentioned techniques guarantees a high level of detection accuracy while meeting the criteria for real-time obstacle identification in unmanned agricultural equipment during fieldwork. Full article

Plastic pollution caused by mulching film residues (MFRs) is escalating in arable lands, which affects the function of agricultural ecosystems, and poses a serious obstacle to agricultural sustainable development in arid regions. Internationally, increasing recycling rate of polyethylene (PE) film and adopting biodegradable films are recommended strategies to mitigate plastic pollution in farmland, aiming to increase agricultural sustainability and food security. However, impacts of the future of these strategies remain underexplored. This study estimated MFRs accumulation over the next 50 years under varying PE and polybutylene adipate terephthalate (PBAT) film recovery scenarios: no recovery, and recovery rates increased to 80%, 85%, 90%, and 95%. Additionally, cumulative ecological effects (CEEs) of MFR pollution were assessed based on historical MFRs accumulations of 75 kg hm⁻², 160 kg hm⁻², 220 kg hm⁻², 300 kg hm⁻², and 400 kg hm⁻², by evaluating direct and indirect ecological effects. The findings revealed that (1) with no recovery, PE film residues could increase by 480 kg hm⁻², whereas achieving a 95% recovery rate could limit residues increasing to below the national threshold of 75 kg hm⁻², outperforming the 80%, 85%, and 90% recovery rates. On the other hand, using PBAT film would maintain the increasing MFRs below 75 kg hm⁻² regardless of recovery rate. (2) Without PE film recovery, CEEs would intensify significantly, as both the direct and indirect effects increase notably, while the CEEs of MFRs could maintain the current status or decrease under the strategy of 95% recovery rate of PE film and using PBAT film, similar to the variation of direct effects. However, indirect effects would persist due to ongoing microplastics (MPs) and phthalate esters (PAEs) released from residual films. Overall, a 95% PE film recycling rate and PBAT film usage emerged as particularly effective strategies for minimizing MFRs accumulation and mitigating ecological impacts over the next 50 years. Further research should prioritize the indirect ecological effects of MFRs, given their persistence despite reduction efforts. The results could provide a theoretical support for agricultural sustainable development in arid regions. Full article

The main causes of habitat conversion, degradation, and fragmentation—all of which add to the loss in biodiversity—are human activities, such as urbanization and farmland reclamation. In order to inform scientific land management and biodiversity conservation strategies and, therefore, advance sustainable development, it is imperative to evaluate the effects of land-use changes on biodiversity, especially in areas with high biodiversity. Using data from five future land-use scenarios under various Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs), this study systematically assesses the characteristics of land-use and landscape pattern changes in southwest China by 2050. This study builds a comprehensive biodiversity index and forecasts trends in species richness and habitat quality using models like Fragstats and InVEST to evaluate the overall effects of future land-use changes on biodiversity. The research yielded the subsequent conclusions: (1) Grasslands and woods will continue to be the primary land uses in southwest China in the future. But the amount of grassland is expected to decrease by 11,521 to 102,832 km², and the amounts of wasteland and urban area are expected to increase by 8130 to 16,293 km² and 4028 to 19,677 km², respectively. Furthermore, it is anticipated that metropolitan areas will see an increase in landscape fragmentation and shape complexity, whereas forests and wastelands will see a decrease in these aspects. (2) In southwest China, there is a synergistic relationship between species richness and habitat quality, and both are still at relatively high levels. In terms of species richness and habitat quality, the percentage of regions categorized as outstanding and good range from 71.63% to 74.33% and 70.13% to 75.83%, respectively. The environmental circumstances for species survival and habitat quality are expected to worsen in comparison to 2020, notwithstanding these high levels. Western Sichuan, southern Guizhou, and western Yunnan are home to most of the high-habitat-quality and species-richness areas, while the western plateau is home to the majority of the lower scoring areas. (3) The majority of areas (89.84% to 94.29%) are forecast to undergo little change in the spatial distribution of biodiversity in southwest China, and the general quality of the ecological environment is predicted to stay favorable. Except in the SSP1-RCP2.6 scenario, however, it is expected that the region with declining biodiversity will exceed those with increasing biodiversity. In comparison to 2020, there is a projected decline of 1.0562% to 5.2491% in the comprehensive biodiversity index. These results underscore the major obstacles to the conservation of biodiversity in the area, highlighting the need to fortify macro-level land-use management, put into practice efficient regional conservation plans, and incorporate traditional knowledge in order to save biodiversity. Full article

Electric multirotor plant protection unmanned aerial vehicles (UAVs) are widely used in China for efficient and precise plant protection at low altitude for low volumes. Unstructured farmland in China has various types of obstacles, and UAVs usually use a detour path to avoid obstacles due to flight altitude limitations. However, existing UAV spray systems do not spray when in obstacle neighborhoods during obstacle avoidance, resulting in insufficient droplet coverage and reduced plant protection quality in the area. To improve the droplet coverage in obstacle neighborhoods, this article carries out a study of side spray technology with an electric quadrotor UAV, and proposes the design and development of a side spray device. The relationship between the obstacle avoidance path of the UAV and the spray pattern of the side spray device and their effect on droplet coverage in obstacle neighborhoods was explored. An accurate measurement method of the relative position between the UAV and obstacles was proposed. Spray angle calculations and nozzle selection for the side spray device were carried out in conjunction with the relative position. A rotor wind field simulation model was designed based on the lattice Boltzmann method (LBM), and the spatial layout of the side spray device on the UAV was designed based on the simulation results. To explore suitable spray patterns for the side spray device, comparative experiments of droplet coverage in obstacle neighborhoods were carried out under different environments, spray patterns, and flight parameter combinations. The relationship between the flight parameter combinations and the distribution uniformity of droplets and the effective swath width of the side spray device was explored. The experimental results were analyzed by an analysis of variance (ANOVA) and a relationship model was obtained. The results showed that the side spray device can effectively improve droplet coverage in obstacle neighborhoods compared to a device without side spray using the same flight parameter combinations. The effective swath width in obstacle neighborhoods can be increased by a minimum of 6.35%, maximum of 35.32%, and average of 15.25% using the side spray device. The error between the predicted values of the relational model and the field experiment results was less than 15%. The results verify the effectiveness and rationality of the method proposed in this article. This study can provide technical and theoretical references for improving the plant protection quality of UAVs in obstacle environments. Full article

Degradation of black soil areas is a serious threat to national food security and ecological safety; nevertheless, the current lack of information on the location, size, and condition of black soil farmland productivity is a major obstacle to the development of strategies for the sustainable utilization of black soil resources. We synthesized remote sensing data and geospatial thematic data to construct a farmland productivity assessment indicator system to assess the productivity of black soil cropland at the regional scale. Furthermore, we conducted research on the spatial differentiation patterns and a spatial autocorrelation analysis of the assessment results. We found that farmland productivity within this region exhibited a decline pattern from south to north, with superior productivity in the east as opposed to the west, and the distribution follows a “spindle-shaped” pattern. Notably, the Songnen and Sanjiang typical black soil subregions centrally hosted about 46.17% of high-quality farmland and 53.51% of medium-quality farmland, while the Mondong typical black soil subregion in the west predominantly consisted of relatively low-quality farmland productivity. Additionally, farmland productivity displayed a significant positive spatial correlation and spatial clustering, with more pronounced fluctuations in the northeast–southwest direction. The developed indicator system for farmland productivity can illustrate the spatial differentiation and thereby offer a valuable reference for the sustainable management of farmland resources. Full article

Unmanned tractors under ploughing conditions suffer from body tilting, violent shaking and limited hardware resources, which can reduce the detection accuracy of unmanned tractors for field obstacles. We optimize the YOLOv8 model in three aspects: improving the accuracy of detecting tilted obstacles, computational reduction, and adding a visual ranging mechanism. By introducing Funnel ReLU, a self-constructed inclined obstacle dataset, and embedding an SE attention mechanism, these three methods improve detection accuracy. By using MobileNetv2 and Bi FPN, computational reduction, and adding camera ranging instead of LIDAR ranging, the hardware cost is reduced. After completing the model improvement, comparative tests and real-vehicle validation are carried out, and the validation results show that the average detection accuracy of the improved model reaches 98.84% of the mAP value, which is 2.34% higher than that of the original model. The computation amount of the same image is reduced from 2.35 billion floating-point computations to 1.28 billion, which is 45.53% less than the model computation amount. The monitoring frame rate during the movement of the test vehicle reaches 67 FPS, and the model meets the performance requirements of unmanned tractors under normal operating conditions. Full article

The high-flux acquisition of crop growth information can be realized using field monitoring robotic platforms. However, most of the existing agricultural monitoring robots have been converted from expensive commercial platforms, and they thus have a hard time adapting to the farmland working environment, let alone satisfying the basic requirements of sensor testing. To address these problems, a wheeled crop-growth-monitoring robot that features the accurate, nondestructive, and efficient acquisition of crop growth information was developed based on the cultivation characteristics of wheat, the obstacle characteristics of the wheat field, and the monitoring mechanism of spectral sensors. By analyzing the phenotypic structural change characteristics and the requirements for the row spacing of different wheat varieties throughout the growth period, a four-wheel mobile chassis was designed with an adjustable wheel track and a high-clearance body structure that can effectively eliminate the risk of the robot destroying the wheat during operation. Moreover, considering the requirements for wheeled robots to overcome obstacles in field operations, a three-dimensional (3D) model of the robot was created in Pro/E. Models of obstacles in the field (e.g., pits and bumps) were created in Adams to simulate the operational stability of the robot. The simulation results showed that the mass center displacement of the robot was smaller than 0.2 cm on flat pavement and the maximum mass center displacement was 1.78 cm during obstacle crossing (10 cm deep pits and 10 cm high bumps). The field test showed that the robot equipped with active-light-source crop growth sensors achieved stable, real-time, nondestructive, and accurate acquisition of the canopy vegetation parameters—NDVI (normalized difference vegetation index) and RVI (ratio vegetation index)—and the wheat growth parameters—LAI (leaf area index), LDW (leaf dry weight), LNA (leaf nitrogen accumulation), and LNC (leaf nitrogen content). Full article