Search Results (231)

To address the bottlenecks of low water and fertilizer utilization efficiency and limited yield potential inherent in Henan Province’s traditional winter wheat cultivation model of “furrow irrigation + conventional row seeding”, this study delved into the synergistic regulatory mechanisms of drip irrigation combined with wide-row precision seeding. It focused on their effects on the physiological ecology and yield-quality traits of winter wheat. A two-factor experiment, encompassing “sowing method × irrigation method” will be carried out during the 2024–2025 wheat growing season, featuring four treatments: furrow irrigation + conventional row seeding (QT), drip irrigation + conventional row seeding (DT), furrow irrigation + wide-row precision seeding (QK), and drip irrigation + wide-row precision seeding (DK). Results reveal that wide-row precision seeding optimized the canopy structure, raising the leaf area index (LAI) at the heading stage by 20.19% compared to QT, thereby enhancing ventilation and light penetration and reducing plant competition. Drip irrigation, with its precise water delivery, boosted the net photosynthetic rate of the flag leaf 35 days after flowering by 62.99% relative to QT, stabilizing root water uptake and significantly delaying leaf senescence. The combined effect of the two treatments (DK treatment) synergistically improved the canopy structure and photosynthetic performance of winter wheat, prolonging the functional period of green leaves by 29.41%. It established a highly efficient photosynthetic cycle, marked by “high stomatal conductance-low intercellular CO₂ concentration-high net photosynthetic rate”. The peak net photosynthetic rate (P_n) 13 days post-flowering rose by 23.9% compared to QT. Moreover, while reducing total water consumption by 21.4%, it substantially increased water use efficiency (WUE) and irrigation water use efficiency (IWUE) by 43.2% and 14.2%, respectively, compared to the QT control. Ultimately, the DK treatment achieved a synergistic enhancement in both yield and quality: grain yield increased by 14.7% compared to QT, wet gluten content reached 35.5%, and total protein yield per unit area rose by 13.1%. This study demonstrates that coupling drip irrigation with wide-row precision seeding is an effective strategy for achieving water-saving, high-yield, and high-quality winter wheat cultivation in the Huang-Huai-Hai region. This is achieved through the synergistic optimization of canopy structure, enhanced photosynthetic efficiency, and improved WUE. These findings provide a mechanistic basis and a scalable agronomic solution for sustainable intensification of winter wheat production under water-limited conditions in major cereal-producing regions. Full article

Cotton is a globally important economic crop and the foundational raw material for the textile industry, and the planting pattern plays a crucial role in determining both the yield and quality of cotton. The results demonstrated that compared with the use of the traditional wide–narrow row (66 + 10 cm) planting pattern, the use of uniform row spacing significantly increased cotton yield (pooled effect size = 0.09, p < 0.05; average yield increase of 9.41%) when interrow distances were homogenized to optimize the population canopy structure. Moreover, this approach comprehensively improved fiber quality, yielding an average increase of 2.02% in cotton fiber length (pooled effect size = 0.02, p < 0.001), an average increase of 8.32% in cotton breaking tenacity (pooled effect size = 0.08, p < 0.001), and an average decrease of 6.76% in the cotton micronaire value (pooled effect size = −0.07, p < 0.001). This study confirms that the use of a uniform row spacing planting pattern is a key agronomic measure for simultaneously achieving high yield and superior fiber quality in cotton, providing both theoretical and practical insights into the optimization of cotton cultivation patterns. Full article

Understanding soil respiration (Rs) dynamics in Mediterranean olive groves is crucial for quantifying carbon fluxes under climate change. Soil respiration represents the combined CO₂ efflux from root metabolic activity and microbial decomposition of soil organic matter, processes strongly controlled by soil moisture, temperature, and the quantity and quality of organic matter inputs in semi-arid Mediterranean environments. This study quantified the seasonal and spatial variability of Rs in a traditional rainfed olive orchard planted at a spacing of 11 m between rows and 9 m between trees (≈101 trees ha⁻¹). Continuous measurements were conducted in two contrasting zones, under-canopy (UC) and inter-row (IR), using automated soil CO₂ flux chambers. Annual Rs reached 3.68 Mg CO₂ ha⁻¹ y⁻¹ in UC and 2.21 Mg CO₂ ha⁻¹ y⁻¹ in IR, with substantially higher emissions per unit area beneath the canopy. However, due to its larger surface proportion, the IR zone contributed more to the orchard scale CO₂ budget. Soil water content emerged as the dominant environmental driver of Rs, moderating or suppressing the temperature response during dry periods. These findings highlight the importance of explicitly considering microsite heterogeneity when assessing soil CO₂ efflux and designing sustainable carbon-management strategies in Mediterranean olive agroecosystems. Full article

To address the challenges of narrow, confined spaces in traditional closed-canopy orchards, where complex terrain between and within rows hinders the operation of large and medium-sized mowers. A self-propelled intra-plant obstacle-avoiding oscillating mower was developed. Its core innovation is an integrated oscillating mechanism that achieves one-pass, full-coverage operation by coordinating a 110° fan-shaped cutting path for inter-row areas with an adaptive flipping contour-cutting action for intra-plant areas. The power and transmission systems were optimized according to the shear and bending forces of three common weed species. The integrated prototype was then built and subjected to field tests. The results showed that the shear and bending forces of all three weed species peaked at 30 mm from the root and stabilized beyond 50 mm. Field tests demonstrated a 100% intra-plant obstacle passage rate, 96.9% cutting width utilization rate, 92.07% stubble height stability coefficient, and a 1.66% missed-cutting rate, which meets the operational requirements of closed-canopy orchards. Full article

The Satisfiability Problem (SAT), a fundamental NP-complete problem, is widely applied in integrated circuit verification, artificial intelligence planning, and other fields, where the growing scale and complexity of practical problems demand higher solving efficiency. Due to redundant search paths, serialized reasoning steps, and inefficient pure literal detection, traditional serial SAT solvers require efficient parallelization of the pure literal rule. This paper adopts a parallel solving algorithm for the pure literal rule based on matrix representation. The algorithm can solve the shortcomings of poor universality, insufficient parallel collaborative mechanisms, and clause reduction. We first introduce a Clause-Numerical Incidence Matrix (CNIM) representation to provide a unified mathematical model for parallel operations. Second, we design a Column Vectors Pure Literal Parallel Topological Detection (CVPLPTD) algorithm that achieves pure literal detection with $O(mn/p)$ time complexity (p being the number of parallel threads) within the coefficient range $[1.0\times mn/p, 1.2\times mn/p]$. Finally, we adopt a dynamic matrix reduction strategy that compresses the matrix scale through row and column deletion after each pure literal assignment to reduce computational load. These innovations integrate matrix algebra and parallel computing, effectively breaking through the efficiency limitations of solving large-scale SAT problems while ensuring good universality across different computing platforms. Full article

Traditional clean tillage in peach orchards leads to soil degradation and nitrogen (N) loss. While inter-row grassing can optimize N cycling, the specific rhizosphere microbial mechanisms involved have not been fully understood. This study investigated how different inter-row grassing modes influence N availability through microbial communities in a peach orchard. The experiment included a monoculture of Trifolium repens L. (Tr), a monoculture of Lolium perenne L. (Pr), their mixture (TPr), and clean tillage (CK). By combining soil physicochemical analyses, metagenomic sequencing, functional gene quantification, and multivariate statistics, the study systematically examined the impacts of inter-row grassing modes on soil N cycling. The results showed that inter-row grassing modes played a significant role in reshaping N processes. Pr enhanced mineralization and nitrification, increasing inorganic N through specific genes (amoA, hao). Tr, on the other hand, promoted diazotrophs (Bradyrhizobium) and dissimilatory nitrate-reducing bacteria, enhancing biological N fixation and retention. TPr combined these benefits, leading to enhanced nitrification, increased labile carbon, and elevated enzyme activities, creating a complex microbe–gene network that mediated nitrification and denitrification. Overall, inter-row grassing modulates rhizosphere functions by enhancing N cycling through a “carbon input–microbial regulation” mechanism, offering an effective strategy for improving N use efficiency and promoting sustainable orchard management. Full article

Biochar and well-rotted manure are commonly employed materials for sustainable agricultural development, possessing the potential to consistently enhance the yield of monoculture crops. However, their impact on the stability of crop yields in intercropping systems, as well as the microenvironment of the border-row rhizosphere, remains inadequately understood. Consequently, this study utilized corn stover biochar and well-rotted pig manure while minimizing the application of chemical fertilizers to investigate the synergistic effects of biochar and composted manure in augmenting maize yield within a soybean–maize intercropping system and regulating the nitrogen cycle in the border-row rhizosphere under reduced fertilization conditions. In comparison to traditional fertilization, the combination of biochar and manure under reduced fertilization conditions significantly increased the contents of ammonium nitrogen (55%), dissolved organic nitrogen (523%), and particulate organic nitrogen (833%) while simultaneously decreasing the content of mineral-associated organic nitrogen (60%). Additionally, this combination synergistically reduced urease activity (22%) while enhancing the activities of nitrogenase (11%), nitrate reductase (297%), and hydroxylamine reductase (20%). This study establishes a theoretical foundation for elucidating how organically amended materials consistently enhance productivity in intercropping systems and alter nitrogen ecology in border-row rhizospheres, offering new perspectives on sustainable fertilization strategies and crop patterns. Full article

Compared to traditional single/double-row concrete cast-in-place piles or concrete walls commonly used in foundation pit engineering, pre-tensioned prestressed hollow concrete-filled steel tube piles (referred to as prestressed Steel Cylinder Piles, or prestressed SC piles) demonstrate superior advantages including high bearing capacity, light weight, enhanced stiffness, excellent crack resistance, and cost-effectiveness, indicating a promising future in foundation pit engineering. However, current research has paid limited attention to such piles. Only a few experimental studies have focused on their flexural performance. No studies have presented bearing behavior investigations considering soil–pile interactions and the differences between these kinds of piles and traditional piles. To address this gap, this paper conducts a systematic investigation into the bearing performance of prestressed SC piles. A refined finite element analysis model capable of accurately characterizing pile–soil interactions is developed to analyze the mechanical behavior. Subsequently, the elastic foundation beam method recommended by design codes is employed to analyze the internal forces and displacement variations of these piles during excavation. Finally, the predictions by the design code are compared against those from the refined model. Results shows that the established finite element model presents reasonable predictions on monitoring data and experimental results, with deviations in bending moments and deformations within the range of 10–15%; a comparative analysis of different pile types reveals that prestressed SC piles exhibit smaller horizontal displacements and higher bearing capacities; the bending moments and deformations predicted by design methods (elastic foundation beam method) are conservative, with the predicted values significantly higher than those predicted by the refined model. Full article

To address the issues of insufficient responsivity and low imaging contrast of carbon-based HGFET high-sensitivity short-wave infrared (SWIR) detectors under low-light conditions, this paper proposes a high-sensitivity and high-contrast image enhancement algorithm for low-light detection, with FPGA-based hardware verification. The proposed algorithm establishes a multi-stage cooperative enhancement framework targeting key challenges such as low signal-to-noise ratio (SNR), high dark-state noise, and weak target extraction. Unlike traditional direct enhancement methods, the proposed approach first performs defective row-column correction and background noise separation based on dark-state data, which provides a clean foundation for signal reconstruction. Furthermore, an adaptive gamma correction mechanism based on image maximum value is introduced to avoid unnecessary nonlinear transformations in high-contrast regions. During the contrast enhancement stage, an exposure-constrained adaptive histogram equalization strategy is adopted to effectively suppress noise amplification and saturation in low-light scenes. Finally, an innovative dual-mode threshold selection method based on image variance is proposed, which can dynamically integrate the OTSU algorithm with statistical moment analysis to ensure robust background noise separation across both high- and low-contrast scenarios. Experimental results demonstrate that the proposed algorithm significantly improves target contrast in infrared images while preventing detail loss due to overexposure. Under microwatt-level laser power, background noise is effectively suppressed, and both imaging quality and weak target detection capability are substantially enhanced. Full article

Agrivoltaics is a sustainable way to produce both energy and food in developing countries facing rising demand for energy and food and limited access to and availability of land resources. However, in agrivoltaics systems, energy production, crop yield, and the amount of equipment used vary considerably depending on the configuration, which can significantly affect the economic profitability of the system. In addition, there are few studies, especially in West Africa, that assess the economic profitability of switching from agricultural systems or PV power plants to agrivoltaics systems. This study addresses these issues. It assesses the profitability of agrivoltaics system configurations and compares them with traditional agricultural systems and PV power plants, using discount rates ranging from 6% to 12% and considering six indicators: the Net Present Value (NPV), Life Cycle Cost, Levelized Cost of Energy, Profitability Index, Internal Rate of Return, and Payback Period. The results show that high-density agrivoltaics systems with limited spacing between panel tables and rows of tables are more profitable than low-density systems. For the most profitable case, the NPV was EUR 9401.24 at a 12% discount rate, whereas this value is negative when the discount rate reaches 7% for case 1, which is the lowest-density agrivoltaic system. Case 3, which is the highest-density agrivoltaic system and the PV power plant, achieved an NPV of EUR 60,411.88 and EUR 164,732.64, respectively, at a 12% discount rate. Full article

Precision spraying is a crucial goal for modern agriculture to achieve water and fertilizer conservation, reduced pesticide use, high yield, and green and sustainable development. This relies on the accurate identification of crop positions, high-precision path planning, and the positioning and control of intelligent agricultural machinery. For the precision production of corn, this paper proposes a new row detection method based on histogram peak detection and sliding window search, avoiding the issues of deep learning methods that are not conducive to lightweight deployment and large-scale promotion. Firstly, green channel segmentation and morphological operations are performed on high-resolution drone images to extract regions of interest (ROIs). Then, the ROIs are converted to a top-view image using perspective transformation, and a histogram analysis is performed using the find_peaks function to detect multiple peaks corresponding to row positions. Furthermore, a sliding window centered around the peak is constructed to search for complete single-row crop pixels in the vertical direction. Finally, the least squares method is used to fit the row curve, estimating the average row spacing (RowGap) and plant spacing (PlantGap) separately. The experimental results show that the accuracy of row detection reaches 93.8% ± 2.1% (n = 60), with a recall rate of 91.5% ± 1.8% and an F1 score of 0.925 ± 0.018. Under different growth stages, row numbers (6–8 rows), and weed interference conditions, the average row spacing measurement error is better than ±2.5 cm, and the plant spacing error is less than ±3.0 cm. Through field verification, this method reduces pesticide use by 23.6% and water consumption by 21.4% compared to traditional uniform spraying, providing important parameter support for field precision planting quality assessment and the dynamic monitoring of planting density, achieving variable irrigation and fertilization and water resource conservation. Full article

To address the issues of high labor intensity, high missed harvest rates, and high damage rates associated with traditional subsurface crop harvesters, this paper takes carrots as the research object and designs an automatic row-following device based on collaborative perception and intelligent control. Firstly, the physical characteristic parameters and planting agronomic requirements of carrots in a harvest period were systematically measured and analyzed, and a collaborative control architecture with ‘lateral row-following and longitudinal profiling’ as the core was established. The architecture was composed of a lateral detection mechanism and a ridge surface floating detection mechanism. Building on this, this paper designed a control system with a STC12C5A60S2 single-chip microcomputer as the control core and a fusion fuzzy PID algorithm. By collaboratively driving the lateral and vertical stepper motors, the system achieved a precise control of the digging device’s position and posture, significantly improving the response speed and control stability under complex ridge conditions. Through the simulation of SolidWorks (2019) and RecurDyn (2023), the structural reliability and dynamic profiling effect of key components were validated from both static and dynamic perspectives, respectively. The parameter optimization results based on the response surface method show that the lateral motor speed and the forward speed are the dominant factors affecting the lateral accuracy and the vertical accuracy, respectively. Under the optimal parameter combination, the mean lateral deviation of the device measured in the field test was 1.118 cm, and the standard deviation was 0.257 cm. The mean vertical deviation is 0.986 cm, and the standard deviation is 0.016 cm. This study provides a feasible technical solution for the mechanized agronomic operation of carrots and other subsurface crops. Full article

The use of Remotely Piloted Aircraft (RPA) for spraying coffee crops has expanded due to their practicality and cost reduction. This study aimed to evaluate spray rate effects on coffee crops using two RPA (T10 and T20). The study was conducted on a commercial farm with 10-year-old Coffea arabica Catucaí Amarelo. Two aircraft were used, T1 (hydraulic) and T2 (rotary nozzles). The application rates were established at 25 and 50 L ha⁻¹. The application quality was obtained by attaching Water-Sensitive Papers (WSPs) to the upper, middle, and lower parts of coffee trees, inside and outside the plants, in addition to the inter-row areas. The statistical Nested Crossed Design was applied to analyze the dataset for the experimental field with three distinct factors (RPA, application rate, and WSP position) and four replications. WSP position was the most determinant factor across all design effects, followed by RPA. The external layers of leaves received more droplets than internal parts of coffee trees. The WSP position information indicated that no droplets reached the middle interior parts of the plants or underneath them. The inter-row positions (soil) received significantly more drops than the coffee plants, regardless of application rate or RPA. The potential for drift to the soil was high in both applications. The Potential Drift Risks were more significant for RPA T2. Future studies may deepen understanding of the relationship between coverage and specific application models for coffee farming, as traditional application methods require improvements. Full article

Aiming at the problems of complex image background and numerous interference factors faced by visual navigation systems in orchard environments, this paper proposes an orchard navigation line recognition method based on U-Net. Firstly, the drivable areas in the collected images are labeled using Labelme (a graphical tool for image annotation) to create an orchard dataset. Then, the Spatial Attention (SA) mechanism is inserted into the downsampling stage of the traditional U-Net semantic segmentation method, and the Coordinate Attention (CA) mechanism is added to the skip connection stage to obtain complete context information and optimize the feature restoration process of the drivable area in the field, thereby improving the overall segmentation accuracy of the model. Subsequently, the improved U-Net network is trained using the enhanced dataset to obtain the drivable area segmentation model. Based on the detected drivable area segmentation mask, the navigation line information is extracted, and the geometric center points are calculated row by row. After performing sliding window processing and bidirectional interpolation filling on the center points, the navigation line is generated through spline interpolation. Finally, the proposed method is compared and verified with U-Net, SegViT, SE-Net, and DeepLabv3+ networks. The results show that the improved drivable area segmentation model has a Recall of 90.23%, a Precision of 91.71%, a mean pixel accuracy (mPA) of 87.75%, and a mean intersection over union (mIoU) of 84.84%. Moreover, when comparing the recognized navigation line with the actual center line, the average distance error of the extracted navigation line is 56 mm, which can provide an effective reference for visual autonomous navigation in orchard environments. Full article

In the current scenario of universal accessibility of data, organisations face highly complex challenges related to integrating and processing diverse sets of data in order to meet their analytical needs. This review paper analyses traditional and innovative methods used for data storage and integration, with particular focus on their implications for scalability, consistency, and interoperability within an analytical ecosystem. In particular, it contributes a cross-layer taxonomy linking integration mechanisms (schema matching, entity resolution, and semantic enrichment) to storage/query substrates (row/column stores, NoSQL, lakehouse, and federation), together with comparative tables and figures that synthesise trade-offs and performance/governance levers. Through schema mapping solutions addressing the challenges brought about by structural heterogeneity, storage architectures varying from traditional storage solutions all the way to cloud storage solutions, and ETL pipeline integration using federated query processors, the research provides specific attention for the application of metadata management, with a focus on semantic enrichment using ontologies and lineage management to enable end-to-end traceability and governance. It also covers performance hotspots and caching techniques, along with consistency trade-offs arising out of distributed systems. Empirical case studies from real applications in enterprise lakehouses, scientific exploration activities, and public governance applications serve to invoke this review. Following this work is the possibility of future directions in convergent analytical platforms with support for multiple workloads, along with metadata-centric orchestration with provisions for AI-based integration. Combining technological advancement with practical considerations results in an enabling resource for researchers and practitioners seeking the creation of fault-tolerant, reliable, and future-ready data infrastructure. This review is primarily aimed at researchers, system architects, and advanced practitioners who design and evaluate heterogeneous analytical platforms. It also offers value to graduate students by serving as a structured overview of contemporary methods, thereby bridging academic knowledge with industrial practice. Full article