IEEE Transactions on AgriFood Electronics最新文献_第4页

VTC-TrackBot: A Robust Spatiotemporal Framework for Real-Time Chrysanthemum Counting on Mobile Edge Devices VTC-TrackBot：用于移动边缘设备实时菊花计数的健壮时空框架

IEEE Transactions on AgriFood Electronics Pub Date : 2026-09-01 Epub Date: 2026-06-11 DOI: 10.1109/TAFE.2026.3688647

Siyang Zang;Lei Shu;Grzegorz Cielniak;Ru Han;Xing Yang

{"title":"VTC-TrackBot: A Robust Spatiotemporal Framework for Real-Time Chrysanthemum Counting on Mobile Edge Devices","authors":"Siyang Zang;Lei Shu;Grzegorz Cielniak;Ru Han;Xing Yang","doi":"10.1109/TAFE.2026.3688647","DOIUrl":"https://doi.org/10.1109/TAFE.2026.3688647","url":null,"abstract":"Accurate and real-time flower counting is a critical component of digital horticultural management, enabling precise yield estimation and harvest planning. However, applying computer vision in mobile field scenarios remains challenging due to dense cluster occlusion, variable illumination, and severe camera jitter caused by crawler platform vibrations. To address these issues, this article proposes VTC-TrackBot, an integrated edge-computing system for robust chrysanthemum counting. The framework reformulates the counting task as a reliable tracking-by-detection problem through three synergistic innovations. First, we develop an enhanced detector, you only look once–varieties of tea chrysanthemum, which integrates MSPPFCSPC and BoT3 modules to improve the detection of small and heavily occluded flowers, achieving a mean average precision of 86.6%. Second, to overcome identity switching induced by nonlinear platform vibration, we propose an improved observation-centric simple online and realtime tracking algorithm. Diverging from computationally intensive appearance-based methods, our approach incorporates a noise scale adaptive Kalman filter and a soft-weighted observation-centric momentum strategy to maintain robust trajectory consistency under vibration. Third, a dual-line spatiotemporal integration counting strategy is introduced to repair fragmented tracklets across the central blind zone and filter vibration-induced outliers. Extensive field experiments on a custom crawler robot equipped with an NVIDIA Jetson Xavier NX demonstrate that VTC-TrackBot operates at 23 frames per second, with a counting coefficient of determination (<inline-formula><tex-math>$R^{2}$</tex-math></inline-formula>) of 0.983 and a mean relative error of only 4.14%. These results demonstrate that the proposed lightweight spatiotemporal framework provides an effective and practical solution for real-time phenotyping and yield estimation of densely clustered crops in complex agricultural environments.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"4 2","pages":"471-485"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Overcoming Extreme Sample Scarcity: Deep Learning-Based Real-Time Detection of Cattle Mounting Behavior From UAV Imagery and Video 克服极端样本稀缺性：基于深度学习的无人机图像和视频中牛骑行为的实时检测

IEEE Transactions on AgriFood Electronics Pub Date : 2026-09-01 Epub Date: 2026-06-24 DOI: 10.1109/TAFE.2026.3699492

Jianglong Yan;Everton C. Tetila;Liang Zhao;Willian P. Amorim;Jayme G. A. Barbedo

{"title":"Overcoming Extreme Sample Scarcity: Deep Learning-Based Real-Time Detection of Cattle Mounting Behavior From UAV Imagery and Video","authors":"Jianglong Yan;Everton C. Tetila;Liang Zhao;Willian P. Amorim;Jayme G. A. Barbedo","doi":"10.1109/TAFE.2026.3699492","DOIUrl":"https://doi.org/10.1109/TAFE.2026.3699492","url":null,"abstract":"Early detection of mounting behavior in feedlot cattle is essential for preventing injuries and reducing stress. However, manual observation is inefficient and covers only a limited area. To enable large-scale monitoring, we propose an automated detection system that employs a fine-tuned you only look once version 11 (YOLOv11) model on both uncrewed aerial vehicle (UAV) imagery and videos to identify mounting events in real time. A 112-day field experiment was conducted in Mato Grosso do Sul, Brazil, monitoring 110 Nellore cattle. Because this abnormal behavior is rare, the dataset exhibits extreme sample scarcity: from approximately 10 000 UAV images and 12 pieces of video, only 38 frames contained true mounting events. To address this challenge, we developed a hybrid dataset strategy that integrates real samples, augmented samples, and segment anything model-based synthetic data. With this approach, the model achieved good precision, recall, F1-score, and mean average precision (mAP)@0.5 on the small-scale test set, demonstrating strong generalization performance and confirming the feasibility of UAV-based monitoring for animal-welfare applications. The proposed technique showsx high potential for embedded UAV deployment, enabling autonomous and continuous surveillance in precision livestock systems.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"4 2","pages":"540-548"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149133483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Stem-Clampable Frequency-Domain Electrical Impedance Sensor for Real-Time Monitoring of Plant Stem Water Content Dynamics 用于植物茎含水量动态实时监测的可抓取茎频域电阻抗传感器

IEEE Transactions on AgriFood Electronics Pub Date : 2026-09-01 Epub Date: 2026-07-27 DOI: 10.1109/TAFE.2026.3710865

Rebeka Sultana;Yuan Ji;Shihao Yin;Jiming Song;Liang Dong

{"title":"Stem-Clampable Frequency-Domain Electrical Impedance Sensor for Real-Time Monitoring of Plant Stem Water Content Dynamics","authors":"Rebeka Sultana;Yuan Ji;Shihao Yin;Jiming Song;Liang Dong","doi":"10.1109/TAFE.2026.3710865","DOIUrl":"https://doi.org/10.1109/TAFE.2026.3710865","url":null,"abstract":"Stem water content (SWC) is a critical indicator of plant physiological status, irrigation demand, and growth performance, yet its continuous, nondestructive measurement remains challenging. This study presents a clampable, multielectrode, frequency-domain-based electrical impedance sensor designed for real time, direct SWC monitoring in maize stems. The proposed device integrates eight spring-loaded electrodes with a floating-potential switching architecture, eliminating the need for a fixed ground reference and enabling robust electrical coupling to stems with irregular geometry. Two impedance-based sensing metrics—capacitance and phase angle—were systematically evaluated across multiple frequencies and validated against destructive gravimetric SWC measurements. Experimental results demonstrate that phase-angle-based impedance sensing provides superior robustness to electrode placement and stem heterogeneity, yielding lower prediction error and stronger correlation with gravimetric SWC than capacitance-based measurements. Furthermore, spatial averaging enabled by the multielectrode configuration further enhances measurement stability. These results establish a phase-angle-based, multielectrode impedance sensor as a distinct and reliable approach for continuous SWC sensing, offering a practical pathway toward direct monitoring of plant water status and the development of next-generation wearable plant sensors.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"4 2","pages":"680-689"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Visual Approach for Estimating Plant Growth During the Life Cycle of a Vineyard 一种估算葡萄园生命周期内植物生长的可视化方法

IEEE Transactions on AgriFood Electronics Pub Date : 2026-03-01 Epub Date: 2026-03-16 DOI: 10.1109/TAFE.2026.3665058

A. Bono;G. Vivaldi;C. Guaragnella;T. D’Orazio

{"title":"A Visual Approach for Estimating Plant Growth During the Life Cycle of a Vineyard","authors":"A. Bono;G. Vivaldi;C. Guaragnella;T. D’Orazio","doi":"10.1109/TAFE.2026.3665058","DOIUrl":"https://doi.org/10.1109/TAFE.2026.3665058","url":null,"abstract":"In recent years, the development of automated methods for phenotypic assessments of observable plant traits is gaining interest because they can provide advantages over standard ones. This study presents a novel methodology to estimate the canopy volume of grapevine plants during their growth cycle using a low-cost red, green, blue and depth (RGB-D) sensor mounted on a ground-based platform. The aim is to address the need for cost-effective and easy-to-use systems that can operate under variable environmental conditions without requiring highly specific data acquisition constraints and advanced technical skills. Using a Microsoft Azure Kinect RGB-D camera, detailed 3-D images of the plants are captured. The iterative closest point (ICP) algorithm is then applied to reconstruct a full view of the plants useful to estimate the canopy volumes. The effectiveness of this approach is validated by comparing the volumetric estimates with the leaf area index (LAI) measures obtained with traditional agronomic techniques during significant phenological periods of the plants’ lifecycle. The results demonstrate a correlation between the two approaches, indicating the reliability of the proposed method and highlighting advantages in terms of precision. These outcomes demonstrate the potential of automated vineyard monitoring systems, providing reliable data to assess plant growth conditions.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"4 1","pages":"395-408"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11434973","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147667662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Managing Interplant Competition in Prickly Pear Cactus Fields Using Microwave Technology 利用微波技术管理刺梨仙人掌田的种间竞争

IEEE Transactions on AgriFood Electronics Pub Date : 2026-03-01 Epub Date: 2025-12-02 DOI: 10.1109/TAFE.2025.3635534

Euzeli C. dos Santos;Josinaldo Lopes Araujo Rocha;Anielson dos Santos Souza;Fabiano Salvadori;Isaac Soares de Freitas

{"title":"Managing Interplant Competition in Prickly Pear Cactus Fields Using Microwave Technology","authors":"Euzeli C. dos Santos;Josinaldo Lopes Araujo Rocha;Anielson dos Santos Souza;Fabiano Salvadori;Isaac Soares de Freitas","doi":"10.1109/TAFE.2025.3635534","DOIUrl":"https://doi.org/10.1109/TAFE.2025.3635534","url":null,"abstract":"Microwave-based weed control offers a sustainable alternative to chemical herbicides. This study investigates the feasibility of applying microwave technology for selective weed management in prickly pear cactus plantations, with a focus on minimizing thermal damage to the crop while maximizing weed suppression. A chamber–waveguide device operating at 2.45 GHz was used under controlled conditions to evaluate plant physiological responses, heating dynamics, and optimal orientation for energy delivery. Comparative energy analyses were conducted between the proposed solar-powered autonomous microwave system and traditional tractor-based herbicide application. This analysis reveals that the traditional method requires substantially higher energy input than the proposed microwave-based approach, while relying exclusively on nonrenewable fossil resources. Indeed, the energy embodied in herbicide production, transportation, and tractor-based application contributes to greenhouse gas emissions and environmental degradation. On the other hand, the proposed system is powered by solar energy, a renewable and locally accessible resource that avoids the environmental burdens associated with fossil fuels.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"4 1","pages":"234-242"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147696201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can ML Enhance the Accuracy of Crop Quality Assessment With Radio Frequency Reflectometry? ML能提高射频反射法作物质量评价的准确性吗？

IEEE Transactions on AgriFood Electronics Pub Date : 2026-03-01 Epub Date: 2025-11-12 DOI: 10.1109/TAFE.2025.3624544

Adeoluwa Oyinlola;Temitope Odedeyi

{"title":"Can ML Enhance the Accuracy of Crop Quality Assessment With Radio Frequency Reflectometry?","authors":"Adeoluwa Oyinlola;Temitope Odedeyi","doi":"10.1109/TAFE.2025.3624544","DOIUrl":"https://doi.org/10.1109/TAFE.2025.3624544","url":null,"abstract":"This article presents a novel approach that integrates AI/ML algorithms-linear regression, random forest, and principal component analysis (PCA) to enhance the predictive accuracy of radio frequency reflectometric (RF-R) analysis for crop quality estimation. Using 131 samples of cassava roots, RF-R measurements were carried out within 30 kHz–200 MHz frequency range to estimate dry matter as a proxy for starch content. Multiple modeling strategies were evaluated, including iterative linear regression, random forest, and PCA, which were applied to both single-frequency and multifrequency combinations. A single-frequency analysis from simple linear regression achieved an <inline-formula><tex-math>$R^{2}$</tex-math></inline-formula> of 0.64, while, for the first time, we show that combining two and three frequencies for a random forest model enhances prediction accuracy by 23.4%, highlighting the potential for improved crop quality assessment. Overall, this work demonstrates that combining RF-R with ML improves the specificity and robustness of quality prediction, establishing a foundation for application in real-time, nondestructive crop assessment.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"4 1","pages":"109-116"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VisionTreeS: A Hybrid Tree-Based Visual Masked Autoencoder Approach for Strawberry Yield Forecasting From Low-Resolution Data VisionTreeS：一种基于混合树的视觉掩蔽自编码器方法，用于从低分辨率数据中预测草莓产量

IEEE Transactions on AgriFood Electronics Pub Date : 2026-03-01 Epub Date: 2025-10-03 DOI: 10.1109/TAFE.2025.3613081

Matthew Beddows;Aiden Durrant;Georgios Leontidis

{"title":"VisionTreeS: A Hybrid Tree-Based Visual Masked Autoencoder Approach for Strawberry Yield Forecasting From Low-Resolution Data","authors":"Matthew Beddows;Aiden Durrant;Georgios Leontidis","doi":"10.1109/TAFE.2025.3613081","DOIUrl":"https://doi.org/10.1109/TAFE.2025.3613081","url":null,"abstract":"Accurate crop yield forecasting is crucial in agriculture, yet real-world data is often low-resolution and lacks important features. This article introduces VisionTreeS, a novel hybrid approach combining VisionTS with traditional ML methods (XGBoost and Random Forest) to improve agricultural yield forecasting. While VisionTS excels at temporal pattern recognition with harvest timing errors of 1.33 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 1.35 weeks, it shows higher total yield errors (0.45 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 0.26). Traditional ML like XGBoost provides stronger yield quantity predictions (0.43 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 0.37 total yield error) but struggles temporally, showing harvest window errors up to 7.60 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 5.40 weeks. Our hybrid methods, VisionTreeS-XGB and VisionTreeS-RF, utilize complementary strengths through a scaling mechanism preserving each model’s advantages. VisionTreeS-RF achieves the best total yield error of 0.27 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 0.32, while VisionTreeS-XGB shows 0.31 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 0.29. Both maintain VisionTS’s superior temporal predictions: 1.33 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 1.35 weeks harvest timing accuracy and 0.80 <inline-formula><tex-math>$pm$</tex-math></inline-formula> 1.05 weeks harvest window error. Testing across multiple farm plots demonstrates our combined approaches consistently outperform single-model solutions for both yield and timing predictions. These improvements, particularly with limited data, make our hybrid methods valuable for real-world agricultural forecasting applications.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"4 1","pages":"128-136"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148511933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Circuits and Systems Society Publication Information IEEE电路与系统协会出版信息

IEEE Transactions on AgriFood Electronics Pub Date : 2026-03-01 Epub Date: 2026-04-14 DOI: 10.1109/TAFE.2026.3681995

引用次数: 0

Investigating Channel Models for Galvanic and Capacitive Coupling for Intrabody Plant Area Network (PlANt) Communications 研究体内植物区域网络（Plant）通信中电流和电容耦合的通道模型

IEEE Transactions on AgriFood Electronics Pub Date : 2026-03-01 Epub Date: 2025-11-05 DOI: 10.1109/TAFE.2025.3624195

Aaron Roopnarine;Sean Rocke;Megan Mahadeo;Jeevan Persad

{"title":"Investigating Channel Models for Galvanic and Capacitive Coupling for Intrabody Plant Area Network (PlANt) Communications","authors":"Aaron Roopnarine;Sean Rocke;Megan Mahadeo;Jeevan Persad","doi":"10.1109/TAFE.2025.3624195","DOIUrl":"https://doi.org/10.1109/TAFE.2025.3624195","url":null,"abstract":"Plant area networks (PlANt) refer to the networking infrastructure within smart agriculture at the plant level. They leverage organic communication channels for data exchange within plant structures, inspired by human body communication techniques, offering immense potential when applied to smart agriculture use cases. This study investigates channel models for galvanic and capacitive communication modes in PlANt to optimize communication performance and evaluate their feasibility for intraplant communication use cases. Finite element analysis, conducted using COMSOL Multiphysics, was performed on a plant model derived from the literature, investigating parametric variations of physical dimensions, environment, and electrode configurations, using the Debye–Cole dual-dispersion model for dielectric behavior. Subsequently, using the optimal electrode configurations for each communication mode, empirical channel models were derived for <italic>Artocarpus camansi</i>, <italic>Citrus sinensis</i>, and <italic>Saccharum officinarum</i> plants. An error performance analysis was then conducted on these channel models using various modulation schemes to evaluate communication feasibility. The results show that both galvanic and capacitive coupling are viable for PlANt, with performance dependent on plant-specific parameters and environmental conditions. Furthermore, the IEEE 802.15.6 standard modulation scheme is the most suitable for PlANt communication. Future work aims to explore a broader range of plant species, dynamic growth stages, and environmental variations to improve the generalizability of the channel models. In addition, more advanced modulation schemes and hybrid coupling mechanisms should be investigated for enhanced channel resilience and throughput across diverse agricultural applications.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"4 1","pages":"185-199"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Microwave and Physical–Chemical Characterization of Olive Trees 橄榄树的微波与理化联合表征

IEEE Transactions on AgriFood Electronics Pub Date : 2026-03-01 Epub Date: 2025-11-18 DOI: 10.1109/TAFE.2025.3625744

Alessandro Fedeli;Andrea Randazzo;Diego Villa;Giulia D'Agostino;Alessandro Alberto Casazza

{"title":"Joint Microwave and Physical–Chemical Characterization of Olive Trees","authors":"Alessandro Fedeli;Andrea Randazzo;Diego Villa;Giulia D'Agostino;Alessandro Alberto Casazza","doi":"10.1109/TAFE.2025.3625744","DOIUrl":"https://doi.org/10.1109/TAFE.2025.3625744","url":null,"abstract":"The characterization of trees is important to design and apply nondestructive electronic and electromagnetic diagnostic systems for assessing their health status and capacity of producing fruits. This article focuses on the study of the internal trunk of olive trees, from both the electromagnetic and physical–chemical points of view. To this end, a set of samples has been prepared from a freshly cut olive tree. In particular, eight cylindrical samples have been analyzed with a short-circuited coaxial line to measure the radial, longitudinal, and transverse components of the complex dielectric permittivity tensor at microwave frequencies, extracting the corresponding Debye models. The measured dielectric properties have also been compared with those of other tree species, available in the scientific literature. Moreover, the physical–chemical profile of the trunk has been defined, with particular reference to the moisture content, a fundamental factor that influences the dielectric properties. Moreover, the ash content of the same samples has been analyzed, and both the thermal gravimetric analysis and the Fourier-transform infrared spectroscopy have been performed to further characterize the considered tree.","PeriodicalId":100637,"journal":{"name":"IEEE Transactions on AgriFood Electronics","volume":"4 1","pages":"91-98"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148514099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0