bluuebunny/crossref_metadata_2025 huggingface dataset:
Parallel PSO for Efficient Neural Network Training Using GPGPU and Apache Spark in Edge Computing Sets
Authors: Capel Manuel I. , Salguero-Hidalgo Alberto , Holgado-Terriza Juan A.
Published: August 2024

The training phase of a deep learning neural network (DLNN) is a computationally demanding process, particularly for models comprising multiple layers of intermediate neurons.This paper presents a novel approach to accelerating DLNN training using the particle swarm optimisation (PSO) algorithm, which exploits the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimisation method whose objective is to iteratively enhance the solution to a (usually complex) problem by approximating a given objective. The expensive fitness evaluation and updating of particle positions can be supported more effectively by parallel processing. Nevertheless, the parallelisation of an efficient PSO is not a simple process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In this study, two forms of parallelisation have been developed for the PSO algorithm, both of which are designed for execution in a distributed execution environment. The synchronous parallel PSO implementation guarantees consistency but may result in idle time due to global synchronisation. In contrast, the asynchronous parallel PSO approach reduces the necessity for global synchronization, thereby enhancing execution time and making it more appropriate for large datasets and distributed environments such as Apache Spark. The two variants of PSO have been implemented with the objective of distributing the computational load supported by the algorithm across the different executor nodes of the Spark cluster to effectively achieve coarse-grained parallelism. The result is a significant performance improvement over current sequential variants of PSO.
Addressing EMI and EMF Challenges in EV Wireless Charging with the Alternating Voltage Phase Coil
Authors: Shafiq Zeeshan , Li Tong , Xia Jinglin , Li Siqi , Yang Xi , Zhao Yu
Published: August 2024

Wireless charging technologies are widely used in electric vehicles (EVs) due to their advantages of convenience and safety. Conventional wireless charging systems often use planar circular or square spiral windings, which tend to produce strong electric fields (E-fields), leading to electromagnetic interference (EMI) and potential health risks. These standard coil configurations, while efficient in energy transfer, often fail to address the critical balance between E-field emission reduction and power transfer effectiveness. This study presents an “Alternating Voltage Phase Coil” (AVPC), an innovative coil design that can address these limitations. The AVPC retains the standard dimensions of traditional square coils (400 mm in length and width, with a 2.5 mm wire diameter and 22 turns), but introduces a novel current flow pattern called Sequential Inversion Winding (SIW). This configuration of the winding significantly reduces E-field emissions by altering the sequence of current through its loops. Rigorous simulations and experimental evaluations have demonstrated the AVPC’s ability to lower E-field emissions by effectively up to 85% while maintaining charging power. Meeting stringent regulatory standards, this advancement in the proposed coil design method provides a way for WPT systems to meet stringent regulatory standards requirements while maintaining transmission capability.
The Effect of Ethical Leadership on Innovative Work Behaviors: A Mediating–Moderating Model of Psychological Empowerment, Job Crafting, Proactive Personality, and Person–Organization Fit
Authors: Abuzaid Ahmad Nasser , Ghadi Mohammed Yasin , Madadha Saif-aldeen Marwan , Alateeq Manal Mohammad
Published: August 2024

The study assesses a model designed to investigate the mediating impact of psychological empowerment, job crafting, and proactive personality, and to examine the moderating influence of person–organization fit on the relationship between ethical leadership and employee innovative behavior. A sample of 782 full-time employees from various industries in Jordan were surveyed to gather data on ethical leadership, innovative work behaviors, psychological empowerment, job crafting, proactive personality, and person–organization fit. The study employed an empirical research design, with data collected through surveys. The results reveal a positive correlation between ethical leadership and innovative work behavior, with psychological empowerment, job crafting, and proactive personality as the mediators in this relationship. The link between ethical leadership and innovation work behaviors is also moderated by person–organization fit. The study’s model suggests that ethical leadership practices enhance innovation. Prioritizing ethical principles, transparency, fairness, trust, and accountability cultivates a culture valuing ethics and encouraging innovation. The results provide insights to boost empowerment and proactive behaviors and highlight the importance of a person–organization fit that aligns values for an innovation-friendly workplace. Fit considerations should also be incorporated in recruitment and retention processes. The study makes significant theoretical contributions by synthesizing insights from ethical leadership theory and developing a comprehensive framework to understand how ethical leadership influences innovative work behavior. The research also extends prior work by examining the moderating role of person–organization fit by emphasizing the importance of aligning individual and organizational values in fostering innovation.
Assembly Simulation and Optimization Method for Underconstrained Frame Structures of Aerospace Vehicles
Authors: Li Jinyue , Zhao Gang , Wei Jinhua , Hu Zhiyuan , Zhang Wenqi , Zhang Pengfei
Published: August 2024

Aerodynamic contour dimensional accuracy is very important for the stable and safe flight of aerospace vehicles. Nevertheless, due to the influence of various factors such as material properties, machining and manufacturing deviations, and assembly and installation deviations, key structural geometric dimensions are frequently exceeded. Therefore, this paper investigates a data-driven combined vector loop method (VLM)–Skin Model Shapes (SMS) method to realize aerospace vehicle structural geometric accuracy analysis; assembly optimization targeting contour deviation is also achieved. Tests are carried out on a typical aerospace vehicle’s underconstrained structural workpieces to validate the effectiveness of the proposed method.
Enabling High-Power Conditioning and High-Voltage Bus Integration Using Series-Connected DC Transformers in Spacecrafts
Authors: Orts Carlos , Garrigós Ausiàs , Marroquí David , Arrizabalaga Antxon , Franke Andreas
Published: August 2024

This article proposes a photovoltaic power processor for high-voltage and high-power distribution bus, between 300 V and 900 V, to be used in future space platforms like large space stations or lunar bases. Solar arrays with voltages higher than 100 V are not available for space application, being necessary to apply power conversion techniques. The idea behind this is to use series-connected zero-voltage and zero-current unregulated and isolated DC converters to achieve high bus voltage from the existing solar arrays. Bus regulation is then achieved through low-frequency hysteretic control. Topology description, semiconductor selection, design procedure, simulation and experimental validation, including tests in vacuum and partial pressures, are presented.
A Physical and Spectroscopic Survey of the Lunar South Pole with the Galileo Telescope of the Asiago Astrophysical Observatory
Authors: Trabacchin Nicolò , Ochner Paolo , Colombatti Giacomo
Published: August 2024

In recent years, interest in the Moon has grown exponentially, thanks mainly to space programs with strong international cooperation, such as the NASA Artemis program. Several scientific committees have identified the lunar south pole as the region of greatest interest for building a lasting and sustainable human settlement. However, the knowledge we have of this area is still limited. This work aims to provide a general overview of the main physical and morphological features of the lunar south pole and to propose a first iteration of spectroscopic observations within the visible range from the Asiago Astrophysical Observatory, giving a new and different perspective. The objective is to verify the feasibility of an Earth-based spectroscopic survey to detect water and the abundances of other volatiles and elements.
Research on Detection Algorithm of Green Walnut in Complex Environment
Authors: Yang Chenggui , Cai Zhengda , Wu Mingjie , Yun Lijun , Chen Zaiqing , Xia Yuelong
Published: August 2024

The growth environment of green walnuts is complex. In the actual picking and identification process, interference from near-background colors, occlusion by branches and leaves, and excessive model complexity pose higher demands on the performance of walnut detection algorithms. Therefore, a lightweight walnut detection algorithm suitable for complex environments is proposed based on YOLOv5s. First, the backbone network is reconstructed using the lightweight GhostNet network, laying the foundation for a lightweight model architecture. Next, the C3 structure in the feature fusion layer is optimized by proposing a lightweight C3 structure to enhance the model’s focus on important walnut features. Finally, the loss function is improved to address the problems of target loss and gradient adaptability during training. To further reduce model complexity, the improved algorithm undergoes pruning and knowledge distillation operations, and is then deployed and tested on small edge devices. Experimental results show that compared to the original YOLOv5s model, the improved algorithm reduces the number of parameters by 72.9% and the amount of computation by 84.1%. The mAP0.5 increased by 1.1%, the precision increased by 0.7%, the recall increased by 0.3%, and the FPS is 179.6% of the original model, meeting the real-time detection needs for walnut recognition and providing a reference for walnut harvesting identification.
Spatiotemporal Dynamics and Evolution of Grain Cropping Patterns in Northeast China: Insights from Remote Sensing and Spatial Overlay Analysis
Authors: Du Guoming , Han Le , Yao Longcheng , Faye Bonoua
Published: August 2024

Understanding the spatiotemporal patterns and driving mechanisms of cropping patterns’ evolution tailored to local conditions is crucial for the effective allocation of black soil in northeast China and the advancement of agricultural development. This study utilized the Google Earth Engine platform to extract the spatial distribution data of major grain crops in northeast China for the year 2022. Using crop classification data from 2000 to 2022, the spatial overlay analysis method identified cropping pattern types based on spatial and temporal changes. The primary cropping patterns identified were continuous maize cropping, maize–soybean rotation, mixed cropping, and continuous soybean cropping. Simultaneously, this research constructed three distinct crop periods: Period I (2000–2002), Period II (2010–2012), and Period III (2020–2022). Over three periods, these patterns covered 94.73%, 88.76%, and 86.39% of the area, respectively. The evolution of the dominant cropping pattern from Period I to Period II involved the transition from continuous soybean cropping to continuous maize cropping, while from Period II to Period III, the main shift was from continuous maize cropping to maize–soybean mixed cropping. From a spatial perspective, since Period I, maize has increasingly replaced soybean as the dominant crop, with continuous maize cropping expanding northward and continuous soybean cropping contracting. The maize–soybean rotation area also migrated northward, particularly in the core area of the Songnen Plain, evolving mostly into continuous maize cropping. Maize cropping areas exhibited significant regional characteristics, being densely distributed in the Sanjiang Plain and Liaohe Plain, and along major tributaries in northeast China. Consequently, the interplay of the natural environment, economic policies, and agricultural technologies drove these changes. The findings offer valuable insights for optimizing cropping patterns and developing rotation systems in northeast China.
Evaluation of the Impact of Flutriafol on Soil Culturable Microorganisms and on Soil Enzymes Activity
Authors: Roman Diana-Larisa , Matica Mariana Adina , Boros Bianca-Vanesa , Vulpe Constantina-Bianca , Isvoran Adriana
Published: August 2024

Fungicides play a role in managing plant diseases but raise concerns about environmental impact, emphasizing the need to understand and minimize their effects on non-target ecosystems. Flutriafol is a fungicide used to combat fungal diseases in crops. It has two enantiomers that exhibit different levels of efficacy and environmental impact. This study focuses on evaluating the effects of different doses of flutriafol on soil microorganism populations and enzyme activity and the possible specificity of enantiomer interactions with soil enzymes by combining experimental and computational approaches. The effects of different doses of flutriafol on the population of microorganism and on the activity of soil enzymes were experimentally assessed. Molecular docking of the enantiomers with soil enzymes was used to assess the possible stereoselectivity of the interactions. Regardless of the dose used (normal dose recommended by the manufacturer for cereal crops, half this dose, and double dose), flutriafol had no significant impact on soil microbial communities or on catalase activity. The half dose of flutriafol produced increases in the activity of dehydrogenases (8%), phosphatases (26%), and urease (33%) during the first 7 days of incubation. Molecular docking showed that both enantiomers were able to bind to the active sites of dehydrogenases and phosphatases. The average value of the interaction energy observed for (R)-flutriafol with dehydrogenases was −7.85 kcal/mol, compared to −7.45 kcal/mol for the interaction of (S)-flutriafol with these enzymes. Similarly, the interaction energy obtained for the interaction of (R)-flutriafol with phosphatase was −9.16 kcal/mol, compared to −9.04 kcal/mol for the interaction of (S)-flutriafol with this enzyme. This study confirms the need to implement optimized application practices when using flutriafol by considering the enantiomer that is most effective on the target organism and less toxic to non-target ecosystems.
SN-CNN: A Lightweight and Accurate Line Extraction Algorithm for Seedling Navigation in Ridge-Planted Vegetables
Authors: Zhang Tengfei , Zhou Jinhao , Liu Wei , Yue Rencai , Shi Jiawei , Zhou Chunjian , Hu Jianping
Published: August 2024

In precision agriculture, after vegetable transplanters plant the seedlings, field management during the seedling stage is necessary to optimize the vegetable yield. Accurately identifying and extracting the centerlines of crop rows during the seedling stage is crucial for achieving the autonomous navigation of robots. However, the transplanted ridges often experience missing seedling rows. Additionally, due to the limited computational resources of field agricultural robots, a more lightweight navigation line fitting algorithm is required. To address these issues, this study focuses on mid-to-high ridges planted with double-row vegetables and develops a seedling band-based navigation line extraction model, a Seedling Navigation Convolutional Neural Network (SN-CNN). Firstly, we proposed the C2f_UIB module, which effectively reduces redundant computations by integrating Network Architecture Search (NAS) technologies, thus improving the model’s efficiency. Additionally, the model incorporates the Simplified Attention Mechanism (SimAM) in the neck section, enhancing the focus on hard-to-recognize samples. The experimental results demonstrate that the proposed SN-CNN model outperforms YOLOv5s, YOLOv7-tiny, YOLOv8n, and YOLOv8s in terms of the model parameters and accuracy. The SN-CNN model has a parameter count of only 2.37 M and achieves an mAP@0.5 of 94.6%. Compared to the baseline model, the parameter count is reduced by 28.4%, and the accuracy is improved by 2%. Finally, for practical deployment, the SN-CNN algorithm was implemented on the NVIDIA Jetson AGX Xavier, an embedded computing platform, to evaluate its real-time performance in navigation line fitting. We compared two fitting methods: Random Sample Consensus (RANSAC) and least squares (LS), using 100 images (50 test images and 50 field-collected images) to assess the accuracy and processing speed. The RANSAC method achieved a root mean square error (RMSE) of 5.7 pixels and a processing time of 25 milliseconds per image, demonstrating a superior fitting accuracy, while meeting the real-time requirements for navigation line detection. This performance highlights the potential of the SN-CNN model as an effective solution for autonomous navigation in field cross-ridge walking robots.
Production Process Outsourcing, Farmers’ Operation Capability, and Income-Enhancing Effects
Authors: Li Chengze , Zhang Dianwei , Lu Qian , Wei Jiajing , Zhang Qingsong
Published: August 2024

Production process outsourcing not only enhances farmers’ operation capability but also contributes to income growth. Utilizing field survey data from five provinces—Inner Mongolia, Gansu, Ningxia, Henan, and Shaanxi—this study employs an endogenous switching regression model to analyze the impact of production process outsourcing on the enhancement of farmers’ operation capability and the income-enhancing effect. The results reveal the following: (1) Production process outsourcing significantly improves farmers’ operation capability and increases income. (2) A higher degree of adoption of production process outsourcing correlates with greater improvements in farmers’ operation capability. (3) The impact of production process outsourcing on farmers’ operation capability varies with individual endowments; farmers with higher education levels, a larger number of laborers, and smaller planting areas experience more pronounced improvements in management capabilities when participating in outsourcing. (4) Production process outsourcing partially mediates the income-enhancing effect through its influence on farmers’ operation capability. To further promote income growth, it is essential to enhance the agricultural outsourcing market supply system, expand farmers’ access to production service information, and prioritize the development of farmers’ operation capability.
Non-Destructive Detection Method of Apple Watercore: Optimization Using Optical Property Parameter Inversion and MobileNetV3
Authors: Chen Zihan , Wang Haoyun , Wang Jufei , Xu Huanliang , Mei Ni , Zhang Sixu
Published: August 2024

Current methods for detecting apple watercore are expensive and potentially damaging to the fruit. To determine whether different batches of apples are suitable for long-term storage or long-distance transportation, and to classify the apples according to quality level to enhance the economic benefits of the apple industry, it is essential to conduct non-destructive testing for watercore. This study proposes an innovative detection method based on optical parameter inversion and the MobileNetV3 model. Initially, a three-layer plate model of apples was constructed using the Monte Carlo method to simulate the movement of photons inside the apple, generating a simulated brightness map of photons on the apple’s surface. This map was then used to train the MobileNetV3 network with dilated convolution, resulting in a pre-trained model. Through transfer learning, this model was applied to measured spectral data to detect the presence of watercore. Comparative experiments were conducted to determine the optimal transfer strategy for the frozen layers, achieving model accuracy rates of 99.13%, 97.60%, and 95.32% for two, three, and four classifications, respectively. Furthermore, the model parameters were low at 7.52 M. Test results of this study confirmed the effectiveness and lightweight characteristics of the method that combines optical property parameter inversion, the DC-MobileNetV3 model, and transfer learning for detecting apple watercore. This model provides technical support to detect watercore and other internal diseases in apples.
Spatial Mapping of Soil CO2 Flux in the Yellow River Delta Farmland of China Using Multi-Source Optical Remote Sensing Data
Authors: Yu Wenqing , Chen Shuo , Yang Weihao , Song Yingqiang , Lu Miao
Published: August 2024

The spatial prediction of soil CO2 flux is of great significance for assessing regional climate change and high-quality agricultural development. Using a single satellite to predict soil CO2 flux is limited by climatic conditions and land cover, resulting in low prediction accuracy. To this end, this study proposed a strategy of multi-source spectral satellite coordination and selected seven optical satellite remote sensing data sources (i.e., GF1-WFV, GF6-WFV, GF4-PMI, CB04-MUX, HJ2A-CCD, Sentinel 2-L2A, and Landsat 8-OLI) to extract auxiliary variables (i.e., vegetation indices and soil texture features). We developed a tree-structured Parzen estimator (TPE)-optimized extreme gradient boosting (XGBoost) model for the prediction and spatial mapping of soil CO2 flux. SHapley additive explanation (SHAP) was used to analyze the driving effects of auxiliary variables on soil CO2 flux. A scatter matrix correlation analysis showed that the distributions of auxiliary variables and soil CO2 flux were skewed, and the linear correlations between them (r < 0.2) were generally weak. Compared with single-satellite variables, the TPE-XGBoost model based on multiple-satellite variables significantly improved the prediction accuracy (RMSE = 3.23 kg C ha−1 d−1, R2 = 0.73), showing a stronger fitting ability for the spatial variability of soil CO2 flux. The spatial mapping results of soil CO2 flux based on the TPE-XGBoost model revealed that the high-flux areas were mainly concentrated in eastern and northern farmlands. The SHAP analysis revealed that PC2 and the TCARI of Sentinel 2-L2A and the TVI of HJ2A-CCD had significant positive driving effects on the prediction accuracy of soil CO2 flux. The above results indicate that the integration of multiple-satellite data can enhance the reliability and accuracy of spatial predictions of soil CO2 flux, thereby supporting regional agricultural sustainable development and climate change response strategies.
Dual-Source Cooperative Optimized Energy Management Strategy for Fuel Cell Tractor Considering Drive Efficiency and Power Allocation
Authors: Zhang Junjiang , Shi Mingyue , Liu Mengnan , Li Hanxiao , Zhao Bin , Yan Xianghai
Published: August 2024

To solve the problems of the low driving efficiency of a fuel cell tractor power source and the high hydrogen consumption caused by the irrational power allocation of the energy source, the power system was divided into two parts, power source and energy source, and a dual-source cooperative optimization energy management strategy was proposed. Firstly, a general energy efficiency optimization method was designed for the power source composed of a traction motor and PTO motor, and the energy source was composed of a fuel cell and power battery. Secondly, the unified objective function and constraint conditions were established, and the instantaneous optimization algorithm was used to construct the weight factor. The instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were designed, respectively. Finally, with the demand power as the transfer parameter, the instantaneous optimal drive efficiency energy management strategy and the instantaneous optimal equivalent hydrogen consumption energy management strategy were integrated to form a dual-source collaborative optimal energy management strategy. In order to verify the effectiveness of the proposed strategy, a rule-based energy management strategy was developed as a comparison strategy and tested in an HIL test under plowing and rotary plowing conditions. The results show that the average fuel cell efficiency of the proposed strategy increased by 7.86% and 8.17%, respectively, and the proposed strategy’s equivalent hydrogen consumption decreased by 24.21% and 9.82%, respectively, compared with the comparison strategy under the two conditions. It can significantly reduce the SOC fluctuation of the power battery and extend the service life of the power battery.
Migration of Microplastics in the Rice–Duckweed System under Different Irrigation Modes
Authors: Hong Cheng , Wang Zhenchang , Tian Minghao , Wang Yuexiong , Liu Jinjing , Qiang Xiaoman , Masharifov Umidbek , Chen Kexin
Published: August 2024

Microplastic (MP) pollution in agriculture is garnering growing concern due to its potential detrimental impact on soil properties and crop growth, particularly affecting staple food crops such as rice. Irrigation plays a crucial role in the migration of MPs. However, limited research has focused on how different irrigation modes affect the migration of MPs in paddy fields. To simulate real-world conditions, in this experiment, two different irrigation modes were set: shallow–frequent irrigation (FWI, I0) and controlled irrigation (CI, I1). The experiment also included treatments with and without duckweed (D0 and D1, respectively), as well as treatments with and without MPs (M0 and M1). This resulted in a total of eight treatments: I0M0D0, I0M0D1, I1M0D0, I1M0D1, I0M1D0, I0M1D1, I1M1D0, and I1M1D1. Our findings indicated that compared to CI, FWI significantly increased the MP concentration in the leakage but reduced the numbers of MPs in the first soil layer and adhered by duckweed. Notably, dry–wet cycles under CI induced soil cracking, and the MP concentrations in cracked areas were significantly higher than those of crack-free soil. Moreover, compared with the MP-free treatment, MP treatments significantly influenced rice root growth, such as enhancing the average root diameter by 13.44%, root volume by 46.87%, root surface area by 30.81%, and biomass aboveground by 26.13%, respectively. The abundance of some microorganisms was also significantly influenced by the relative mobility (RM) of MPs. Furthermore, the root length was positively correlated with Planctomycetota. Meanwhile, Actinobacteriota was negatively correlated with the root surface area, root volume, and branch number, and Bacteroidota was negatively correlated with the number of root tips. However, further research is needed to elucidate how MPs influence microorganisms and, in turn, affect rice root growth.
Effect of Co-Application of Chinese Milk Vetch and Iron-Modified Biochar on Rice in Antimony-Polluted Soil
Authors: Hu Yejie , Xiang Xinglong , Jiang Wenjie , Meng Guiyuan , Zhou Jing , Guo Zhenzhen , Zhou Jinxiu , Tang Haiying , Miao Jianqun , Morsy Kareem
Published: August 2024

Antimony (Sb) toxicity is a serious concern across the globe due to its hazardous impacts on plants and living organisms. The co-application of Chinese milk vetch (CMV) and biochar (BC) is a common agricultural practice, however, the effects of combined CMV and BC in mitigating Sb toxicity and bio-availability remain unclear. Therefore, this study investigated the impacts of CMV, rape straw biochar (RBC), and iron-modified biochar (FMB) and their combinations on rice productivity, physiological, and biochemical functioning of rice and Sb availability. Antimony toxicity caused a marked reduction in rice growth and productivity by decreasing chlorophyll, and anthocyanin synthesis, leaf water contents, osmolyte synthesis and antioxidant activities while, increasing hydrogen peroxide (H2O2), electrolyte leakage (EL), and malondialdehyde (MDA) production and Sb accumulation. Co-application of CMV and FMB increased biomass (29.50%) and grain yield (51.07%) of rice by increasing chlorophyll, and anthocyanin synthesis, leaf water contents, osmolyte synthesis, antioxidant activities, and decreasing production of H2O2, EL, and MDA and Sb accumulation in roots (90.41%) and shoots (96.38%). Furthermore, the combined addition of CMV and FMB also reduced the soil available Sb by 75.57% which resulted in less accumulation of Sb in plant parts and improved growth and yield. Given these facts, these findings indicate that co-application of CMV and FMB is a promising approach to remediate Sb-polluted soils and improve sustainable and safer rice productivity.
A Hybrid Synthetic Minority Oversampling Technique and Deep Neural Network Framework for Improving Rice Yield Estimation in an Open Environment
Authors: Yuan Jianghao , Zheng Zuojun , Chu Changming , Wang Wensheng , Guo Leifeng
Published: August 2024

Quick and accurate prediction of crop yields is beneficial for guiding crop field management and genetic breeding. This paper utilizes the fast and non-destructive advantages of an unmanned aerial vehicle equipped with a multispectral camera to acquire spatial characteristics of rice and conducts research on yield estimation in an open environment. The study proposes a yield estimation framework that hybrids synthetic minority oversampling technique (SMOTE) and deep neural network (DNN). Firstly, the framework used the Pearson correlation coefficient to select 10 key vegetation indices and determine the optimal feature combination. Secondly, it created a dataset for data augmentation through SMOTE, addressing the challenge of long data collection cycles and small sample sizes caused by long growth cycles. Then, based on this dataset, a yield estimation model was trained using DNN and compared with partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF). The experimental results indicate that the hybrid framework proposed in this study performs the best (R2 = 0.810, RMSE = 0.69 t/ha), significantly improving the accuracy of yield estimation compared to other methods, with an R2 improvement of at least 0.191. It demonstrates that the framework proposed in this study can be used for rice yield estimation. Additionally, it provides a new approach for future yield estimation with small sample sizes for other crops or for predicting numerical crop indicators.
Spraying Wheat Plants with a Drone Moved at Low Altitudes
Authors: Berner Bogusława , Chojnacki Jerzy , Dvořák Jiří , Pachuta Aleksandra , Najser Jan , Kukiełka Leon , Kielar Jan , Najser Tomáš , Mikeska Marcel
Published: August 2024

On a mounted laboratory stand, comparative tests were carried out on the effectiveness of spraying wheat plants with liquid using a multi-rotor drone. The study was undertaken with and without propeller rotations. The lack of rotations simulated spraying by a ground sprayer. The height of the drone’s displacement above the plants was similar to that of the nozzles above the plants used when spraying with field sprayers, 0.5 m and 1.0 m. The speed of the drone movement was 0.57 and 1.0 m·s−1. The effects of the height and speed of the drone’s movement and the impact of the airflow on the volume and uniformity of the liquid application on the plants were assessed. In addition, changes in the transverse distribution of liquid volume in the droplet stream and the transverse distribution of the air velocity in its stream were evaluated. The liquid was sprayed at a constant pressure of 0.2 MPa. The study’s results show that the low height of the drone displacement not only had a strong effect on increasing the liquid volume applied to the plants but also improved the uniformity of application at plant levels. It was also noticed that, at a height of 0.5 m, there was a significant irregularity in the air stream under the drone.
Oxidative Degradation Characteristics of Low-Temperature Pyrolysis Biochar and the Synergistic Effect on Released Nutrients
Authors: Chen Limei , Zhuo Yuchen , Zhou Yaoyu , Chen Qing , Peng Yutao , Liu Haoyuan , Deng Jia , Xiao Jiahong , Ai Wenke , Sun Songlin , Gao Chongfeng , Sun Chaoran
Published: August 2024

Biochar application has received much attention because biochar can be used as an organic amendment. The nutrient release patterns and interactions in straw biochar produced at different temperatures are not well understood. In this study, we observed the release patterns of carbon (C), nitrogen (N), phosphorus (P), and potassium (K) and the interaction between released C, N, P, and K from straw biochar prepared from 225 to 600 °C through a 180-day degradation experiment. The results showed that the degradation rate of the two kinds of straw biochar was faster in the first 30 days at different temperatures, and that of the straw biochar prepared before 300 °C was more rapid, indicating that 300 °C is an important turning point. The rule of nutrient release in the straw biochar showed that the K release rate was the highest and most rapid and was more than 60% in the first 30 days. The nutrient release rates for the two kinds of straw biochar were in the order K > N > P > C. The release of nutrients accompanied the decomposition of the straw biochar, and there was an exponential relationship between the amount of nutrients released from straw biochar and its degradation mass. There were collaborative or similar release processes indicated by significant positive correlations between the released C and N (R2 = 0.96) and P and K (R2 = 0.94) in the tobacco straw biochar and an obvious correlation between the released C and N (R2 = 0.76) in the rice straw biochar. These results indicated that the released C and N, P, and K in tobacco straw biochar, as well as C and N in rice straw biochar, have synergistic effects and the same degradation path. The application of straw biochar can provide a source of P and N in the short term and a source of P and C in the long term. This study suggests that returning straw biochar to the soil could appropriately reduce the input of K fertilizer in the early stage.
Effect of Streptomyces costaricanus Strain A-m1 as a Bioinoculant on Tea Garden Soil and Tea Quality
Authors: Zhou Zhou , Li Yongli , Duan Linlin , Zhou Rui , Wu Feifei , Xiong Mengqin , Zhang Biyao , Geng Shubao , Qiao Li , Zhang Fangmei , Wang Lijuan
Published: August 2024

Chemical fertilization is usually associated with some unreasonable problems that affect the sustainable production of tea gardens. The micro-organism fertilizer created from plant growth-promoting microbes (PGPM) integrates the beneficial properties of functional micro-organisms and bioinoculants. Application of PGPM can activate soil nutrients, prevent soil-borne diseases, and promote crop growth, thus improving crop quality and yield. In this study, the effects of bioinoculants composed of Streptomyces costaricanus strain A-m1 on the properties, enzyme activity, and micro-organisms of soil in a tea garden and on the chemical composition and production of tea were investigated. The present results showed that the application of A-m1 bioinoculant could increase the activities of urease, protease and catalase, the content of alkali-hydrolyzable nitrogen, and the number of bacteria, fungi, and actinomycetes in tea garden soil. After application, the free amino acid content, 100-bud weight, and bud density of spring tea were also elevated. In the year of fertilization, the treatment composed of 70% bioinoculant + 30% chemical fertilizer showed the best effects on soil physical and chemical properties, enzyme activity, culturable microbial counts, and tea quality. A high ratio of organic to chemical fertilizer coapplication can significantly improve the growing conditions for tea plants, reduce the use of chemical fertilizers, improve the efficiency of nutrient utilization, and enhance both the yield and quality of tea. One year after fertilization, the 50% bioinoculant + 50% chemical fertilizer was more conducive to enhancing the quality of tea, while the 30% bioinoculant + 70% chemical fertilizer was more beneficial for improving the production of tea. A high ratio of chemical to bioinoculant coapplication is more favorable for maintaining high yield and quality in tea production, achieving healthy and sustainable tea garden management. The application of A-m1 bioinoculant will reduce the use of chemical fertilizers, improve the utilization efficiency of soil nutrients, and increase the production and quality of tea, contributing to the sustainable production of tea gardens.
Trade-Off and Synergy Mechanism of Agricultural Water Resource Spatial Allocation in Monsoon Climate Areas Based on Machine Learning: A Case Study of Reservoir Layout Optimization in Shandong Province, China
Authors: Zou Mingyu , Xie Danfeng , Xu Liwen , Dai Ke , Liang Shuang , Guo Mengdie , Qin Xushuai , Zhao Weijun
Published: August 2024

Influenced by increasing global extreme weather and the uneven spatiotemporal distribution of water resources in monsoon climate areas, the balance of agricultural water resources supply and demand currently faces significant challenges. Conducting research on the spatial allocation trade-offs and synergistic mechanisms of agricultural water resources in monsoon climate areas is extremely important. This study takes the spatial layout of reservoir site selection in water conservancy projects as an example, focusing on Shandong Province as the research area. During the site selection process, the concept of water resource demand is introduced, and the suitability of reservoir siting is integrated. It clarifies ten influencing factors for suitability degree and five influencing factors for demand. A bi-objective optimization model that includes suitability degree and demand degree is established. Utilizing machine learning methods such as the GA_BP neural network model and the GA-bi-objective optimization model to balance and coordinate the supply and demand relationship of agricultural water resources in the monsoon region. The study found that: (1) in the prediction of suitability degree, the influencing factors are most strongly correlated with the regulatory storage capacity (regulatory storage capacity > total storage capacity > regulating storage coefficient); (2) compared with single-objective optimization of suitability degree, the difference between water supply and demand can be reduced by 74.3% after bi-objective optimization; (3) according to the spatial layout optimization analysis, the utilization of water resources in the central and western parts of Shandong Province is not sufficient, and the construction of agricultural reservoirs should be carried out in a targeted manner. This study provides new ideas for promoting the efficient use of water resources in monsoon climate zones and the coordinated development of humans and nature, reflecting the importance of supply and demand balance in the spatial allocation of agricultural water resources, reducing the risk of agricultural production being affected by droughts and floods.
Characterization and Agronomic Evaluation of 25 Accessions of Chenopodium quinoa in the Peruvian Coastal Desert
Authors: Alania-Choque José , Vásquez-Espinoza Leander Gamiel , Anculle-Arenas Alberto , Bustamente-Muñoz José Luis , Jellen Eric N. , Gutiérrez-Rosales Raymundo O. , Mayta-Anco Mayela Elizabeth
Published: August 2024

Quinoa is a healthy food that possesses high levels of protein that is enriched for dietary essential amino acids. The crop is highly diverse and well-adapted to changing climatic conditions. In spite of being vulnerable to pests and diseases, the development of new resistant varieties is possible. Taking advantage of this genetic variability is crucial for breeding programs, especially to adapt quinoa to the shifting needs of producers. In this study, 25 Peruvian accessions and two commercial varieties were characterized and agronomically evaluated in the Peruvian Pacific desert. Specific methodologies and descriptors of existing crops were used, analyzing a total of 24 quantitative and 23 qualitative variables with 15 repetitions per accession. The data were processed using descriptive statistics and a multivariate analysis. The results showed a high variability in morphological characteristics, with an area under the disease progress curve (AUDPC) of the presence of mildew between 529 and 1725, highlighting ACC06 with a lower severity of mildew. The percentage of saponins varied between 0.04 and 0.21 percent, with ACC06 being the one with the lowest percentage. Regarding the crop yield, it ranged between 0.35 and 8.80 t ha−1, highlighting the high-yielding accessions ACC55 and ACC14. These results were promising for the improvement of quinoa yield in the production conditions of the Peruvian Pacific desert.
Simulation of Maize Growth Under the Applications of Brackish Water in Northwest China
Authors: Tong Changfu , He Rui , Wang Jun , Zheng Hexiang
Published: August 2024

The objective of this study is to assess the suitability of the AquaCrop model for growing maize using brackish water irrigation in Northwest China. Additionally, this study aims to examine how maize utilizes water in various soil layers when irrigated with varying water qualities. The AquaCrop model was calibrated and verified using experimental data from the years 2022 and 2023 in this research. (1) The findings indicated that the AquaCrop model effectively simulated the canopy cover, biomass, and yield of maize when irrigated with brackish water. The validation year’s R2, MAPE, and RMSE values for canopy cover, biomass, and yield of maize were 0.95, 5.36%, and 4.77%, respectively. For biomass, the R2, MAPE, and RMSE values were 0.91, 16.61%, and 2.12 t·hm−2, respectively. For yield, the R2, MAPE, and RMSE values were 0.84, 3.62%, and 0.42 t·hm−2, respectively. (2) Irrigation with water of high mineral content, measured at 1.6 ds/m, as well as with fresh water over the whole reproductive period, resulted in an increased reliance on groundwater for maize cultivation. There was no notable disparity in the usage of various soil layers between the irrigation with alternating freshwater and brackish water. (3) The AquaCrop model simulated the effects of seven different irrigation water quality treatments. It was shown that using water with mineralization levels of 0.5 and 0.8 ds/m resulted in decreased freshwater use without causing a substantial decrease in maize yield and biomass.
Phytochemistry of Argemone ochroleuca Sweet Extracts and Their Inhibitory Effects on Maize Seed Germination
Authors: Mlombo Nezelo T. , Dube Zakheleni P. , Makhubu Fikile N. , Nxumalo Hellen
Published: August 2024

Argemone ochroleuca Sweet is an alien invasive weed dominating most cultivated lands, however, the phytochemicals present in this plant and the effects of these on the germination and growth of economically important crops such as maize are not well-documented. The objective of the study was to characterize the phytochemistry of the shoots and roots of A. ochroleuca and determine whether the extracts could inhibit the germination of maize seeds. The shoots and roots of A. ochroleuca were extracted in water, hexane, and acetone. Ten maize seeds were used in the germination bioassay. A phytochemical analysis was conducted using gas chromatography-mass spectrometry (GC-MS). The effects of the A. ochroleuca water, hexane, or acetone extracts on maize seed germination were concentration and plant-part dependent. The highest reduction was recorded from the water extract with 82%. Identified compounds with high percentages in A. ochroleuca were 9,12-octadecadienoic acid (Z,Z) and 9,12,15-octadecatrienoic acid, (Z,Z,Z)-. The present study indicated that A. ochroleuca extracts suppress the germination of maize seeds, likely due to the presence of both the identified and potentially unidentified phytochemicals that were not detected by the selected method. There is, however, a need to establish the relationship between the phytochemical compounds and the enzymes responsible for germination.
A Study on the 3D Reconstruction Strategy of a Sheep Body Based on a Kinect v2 Depth Camera Array
Authors: Liang Jinxin , Yuan Zhiyu , Luo Xinhui , Chen Geng , Wang Chunxin
Published: August 2024

Non-contact measurement based on the 3D reconstruction of sheep bodies can alleviate the stress response in sheep during manual measurement of body dimensions. However, data collection is easily affected by environmental factors and noise, which is not conducive to practical production needs. To address this issue, this study proposes a non-contact data acquisition system and a 3D point cloud reconstruction method for sheep bodies. The collected sheep body data can provide reference data for sheep breeding and fattening. The acquisition system consists of a Kinect v2 depth camera group, a sheep passage, and a restraining pen, synchronously collecting data from three perspectives. The 3D point cloud reconstruction method for sheep bodies is implemented based on C++ language and the Point Cloud Library (PCL). It processes noise through pass-through filtering, statistical filtering, and random sample consensus (RANSAC). A conditional voxel filtering box is proposed to downsample and simplify the point cloud data. Combined with the RANSAC and Iterative Closest Point (ICP) algorithms, coarse and fine registration are performed to improve registration accuracy and robustness, achieving 3D reconstruction of sheep bodies. In the base, 135 sets of point cloud data were collected from 20 sheep. After 3D reconstruction, the reconstruction error of body length compared to the actual values was 0.79%, indicating that this method can provide reliable reference data for 3D point cloud reconstruction research of sheep bodies.
Mitochondrial DNA of the Arabian Camel Camelus dromedarius
Authors: Manee Manee M. , Al-Shomrani Badr M. , Alqahtani Fahad H.
Published: August 2024

The Camelidae family, ranging from southwest Asia to north Africa, South America, and Australia, includes key domesticated species adapted to diverse environments. Among these, the Arabian camel (Camelus dromedarius) is vital to the cultural and economic landscape of the Arabian Peninsula. This review explores the mitochondrial DNA of the dromedary camel, focusing on the D-loop region to understand its genetic diversity, maternal inheritance, and evolutionary history. We aim to investigate the unique characteristics of Arabian camel mtDNA, analyze the D-loop for genetic diversity and maternal lineage patterns, and explore the implications of mitochondrial genomic studies for camel domestication and adaptation. Key findings on mtDNA structure and variation highlight significant genetic differences and adaptive traits. The D-loop, essential for mtDNA replication and transcription, reveals extensive polymorphisms and haplotypes, providing insights into dromedary camel domestication and breeding history. Comparative analyses with other camelid species reveal unique genetic signatures in the Arabian camel, reflecting its evolutionary and adaptive pathways. Finally, this review integrates recent advancements in mitochondrial genomics, demonstrating camel genetic diversity and potential applications in conservation and breeding programs. Through comprehensive mitochondrial genome analysis, we aim to enhance the understanding of Camelidae genetics and contribute to the preservation and improvement of these vital animals.