Artificial Intelligence in Agriculture最新文献

{"title":"Automatic body temperature detection of group-housed piglets based on infrared and visible image fusion","authors":"Kaixuan Cuan ,&nbsp;Feiyue Hu ,&nbsp;Xiaoshuai Wang ,&nbsp;Xiaojie Yan ,&nbsp;Yanchao Wang ,&nbsp;Kaiying Wang","doi":"10.1016/j.aiia.2025.06.008","DOIUrl":"10.1016/j.aiia.2025.06.008","url":null,"abstract":"<div><div>Rapid and accurate measurement of body temperature is essential for early disease detection, as it is a key indicator of piglet health. Infrared thermography (IRT) is a widely used, convenient, non-intrusive, and efficient non-contact temperature measurement technology. However, the activities and clustering of group-housed piglets make it challenging to measure the individual body temperature using IRT. This study proposes a method for detecting body temperature in group-housed piglets using infrared-visible image fusion. The infrared and visible images were automatically captured by cameras mounted on a robot. An improved YOLOv8-PT model was proposed to detect both piglets and their key body regions (ears, abdomen and hip) in visible images. Subsequently, the Oriented FAST and Rotated BRIEF (ORB) image registration method and the U2Fusion image fusion network were employed to extract temperatures from the detected body parts. Finally, a core body temperature (CBT) estimation model was developed, with actual rectal temperature serving as the gold standard. The temperatures of three body parts detected by infrared thermography were used to estimate CBT, and the maximum estimated temperature based on these body parts (EBT-Max) was selected as the final result. In the experiment, the YOLOv8-PT model achieved a [email protected] of 93.6 %, precision of 93.3 %, recall of 88.9 %, and F1 score of 91.05 %. The average detection time per image was 4.3 ms, enabling real-time detection. Additionally, the mean absolute errors (MAE) and correlation coefficient between EBT-Max and actual rectal temperature is 0.40 °C and 0.6939, respectively. Therefore, this method provides a feasible and efficient approach for group-housed piglets body temperature detection and offers a reference for the development of automated pig health monitoring systems.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 1-11"},"PeriodicalIF":8.2,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595478","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}

{"title":"Corrigendum regarding updated declaration of competing interest statement in previously published articles","authors":"","doi":"10.1016/j.aiia.2026.01.008","DOIUrl":"10.1016/j.aiia.2026.01.008","url":null,"abstract":"","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Page 711"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147394401","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}

{"title":"Multivariate stacked regression pipeline to estimate correlated macro and micronutrients in potato plants using visible and near-infrared reflectance spectra","authors":"Reem Abukmeil ,&nbsp;Ahmad Al-Mallahi ,&nbsp;Felipe Campelo","doi":"10.1016/j.aiia.2025.09.001","DOIUrl":"10.1016/j.aiia.2025.09.001","url":null,"abstract":"<div><div>The ability to sense nutrient status in potato plants using spectroscopy has several merits including the ability to proactively respond to deficiencies of certain elements. While research so far has focused on finding spectral signatures of elements based on their foliar reflectance, the influence of the spectral signatures of the elements on each other in estimating their concentrations in the plant has not been investigated. This work presents a pipeline of stacked regression models capable of accurately estimating nutrient concentrations based on the foliar reflectance. A data set was built from 179 samples of petioles collected across two growing seasons, consisting of the chemical concentrations of 11 nutrients with spectral reflectance values between 400 and 2500 nm. The pipeline consisted of a base layer composed of a multiple univariate linear Lasso regression models to find the initial independent signatures of each nutrient, followed by a layer of nonlinear models to correlate these signatures and account for their interdependencies before finalizing the estimation. The results show that adding this second layer improved estimation performance for 10 and 9 nutrients out of 12 in the dried and fresh mode, respectively, with large improvements in predictive performance for some critical micronutrients such as Zn, Fe, and Al.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 85-93"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264765","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}

{"title":"Energy-saving and stability-enhancing control for unmanned distributed drive electric plant protection vehicle based on active torque distribution","authors":"Wenxiang Xu ,&nbsp;Yejun Zhu ,&nbsp;Maohua Xiao ,&nbsp;Mengnan Liu ,&nbsp;Liling Ye ,&nbsp;Yanpeng Yang ,&nbsp;Ze Liu","doi":"10.1016/j.aiia.2025.11.004","DOIUrl":"10.1016/j.aiia.2025.11.004","url":null,"abstract":"<div><div>The distributed drive electric plant protection vehicle (DDEPPV), equipped with a unique four-wheel independent drive system, demonstrates excellent path-tracking capability and dynamic performance in agricultural environments. However, during actual field operations, issues such as severe tire slip, poor driving stability, high rollover risk, and excessive energy consumption often arise due to improper torque distribution. This study proposes an energy-efficient and stability-enhancing control method based on active torque distribution, aiming to improve both operational safety and system efficiency. A hierarchical control architecture is adopted: the upper-level controller employs a nonlinear model predictive control (NMPC) to achieve coordinated control of steering and yaw moment, enhancing lateral stability and ensuring operational safety. The lower-level controller implements a direct torque allocation method based on an adaptive-weight multi-objective twin delayed deep deterministic policy gradient (AW-MO-TD3) algorithm, enabling joint optimization of tire slip ratio and energy consumption. Real-vehicle tests were conducted under two typical field conditions, and the results show that compared with conventional methods, the proposed strategy significantly improves key performance metrics including tracking accuracy, vehicle stability, and energy efficiency. Specifically, stability was enhanced by 29.1 % and 41.4 %, while energy consumption was reduced by 19.8 % and 21.1 % under dry plowed terrain and muddy rice field conditions, respectively. This research provides technical support for the intelligent control of distributed drive electric agricultural vehicles.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 495-513"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623315","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}

{"title":"PlantSegNeRF: A few-shot, cross-species method for plant 3D instance point cloud reconstruction via joint-channel NeRF with multi-view image instance matching","authors":"Xin Yang ,&nbsp;Ruiming Du ,&nbsp;Hanyang Huang ,&nbsp;Jiayang Xie ,&nbsp;Pengyao Xie ,&nbsp;Leisen Fang ,&nbsp;Ziyue Guo ,&nbsp;Nanjun Jiang ,&nbsp;Yu Jiang ,&nbsp;Haiyan Cen","doi":"10.1016/j.aiia.2025.11.009","DOIUrl":"10.1016/j.aiia.2025.11.009","url":null,"abstract":"<div><div>Organ segmentation of plant point clouds is a prerequisite for the high-resolution and accurate extraction of organ-level phenotypic traits. Although the fast development of deep learning has boosted much research on segmentation of plant point clouds, the existing techniques for organ segmentation still face limitations in resolution, segmentation accuracy, and generalizability across various plant species. In this study, we proposed a novel approach called plant segmentation neural radiance fields (PlantSegNeRF), aiming to directly generate high-precision instance point clouds from multi-view RGB image sequences for a wide range of plant species. PlantSegNeRF performed two-dimensional (2D) instance segmentation on the multi-view images to generate instance masks for each organ with a corresponding instance identification (ID). The multi-view instance IDs corresponding to the same plant organ were then matched and refined using a specially designed instance matching (IM) module. The instance NeRF was developed to render an implicit scene containing color, density, semantic and instance information, which was ultimately converted into high-precision plant instance point clouds based on volume density. The results proved that in semantic segmentation of point clouds, PlantSegNeRF outperformed the commonly used methods, demonstrating an average improvement of 16.1 %, 18.3 %, 17.8 %, and 24.2 % in precision, recall, F1-score, and intersection over union (IoU) compared to the second-best results on structurally complex datasets. More importantly, PlantSegNeRF exhibited significant advantages in instance segmentation. Across all plant datasets, it achieved average improvements of 11.7 %, 38.2 %, 32.2 % and 25.3 % in mean precision (mPrec), mean recall (mRec), mean coverage (mCov), and mean weighted coverage (mWCov), respectively. Furthermore, PlantSegNeRF demonstrates superior few-shot, cross-species performance, requiring only multi-view images of few plants to train models applicable to specific or similar varieties. This study extends organ-level plant phenotyping and provides a high-throughput way to supply high-quality 3D data for developing large-scale artificial intelligence (AI) models in plant science.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 546-564"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623318","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}

{"title":"Organ3DNet: A deep network for segmenting organ semantics and instances from dense plant point clouds","authors":"Dawei Li ,&nbsp;Jiali Huang ,&nbsp;Boyuan Zhao ,&nbsp;Weiliang Wen","doi":"10.1016/j.aiia.2025.10.011","DOIUrl":"10.1016/j.aiia.2025.10.011","url":null,"abstract":"<div><div>Worldwide food shortage has put the plant phenotyping research to the spotlight because phenotyping enhances crop yield under limited land use by accelerating the cycle of modern breeding. The prerequisite of organ-level phenotyping is the accurate segmentation of organs from crop point clouds. Though mainstream deep networks have reported satisfactory results on certain species, they usually require sampling each input point cloud to a fixed number before network learning. Most existing networks also recommend each input to contain less than 10,000 points, which may smooth the structural details and lead to deterioration of segmentation of small organs. Moreover, these methods are still struggling to face challenges such as segmenting crops with a large number of organs and scalability to multiple species. In this paper, we propose Organ3DNet—a novel deep-learning-based architecture tailored for organ segmentation on high-precision plant 3D data. By integrating a Sparse 3D Convolutional Network Backbone (S3DCNB) as encoder and a new Transformer Decoder part containing a cascade of Query Refinement Modules (QRM) and Mask Modules (MM), Organ3DNet begins with query points obtained with 3D Edge-preserving Sampling (3DEPS) and gradually refines those queries into masks to effectively represent different organ instances. A high-precision dataset containing 889 samples from five species is also provided in this study. In experiment on this dataset, our Organ3DNet outcompeted four networks including ASIS, JSNet, PlantNet, and PSegNet. On the organ semantic segmentation task, our method surpasses the second best JSNet by 2.10 % on F1 and 3.63 % on IoU; while on the instance segmentation task, Organ3DNet surpasses the second best PSegNet by large margins of 16.46 % on mCov and 13.44 % on mWCov, respectively. Validation tests also show that Organ3DNet performs well on downstream tasks and real agricultural scenarios. The dataset and code associated with Organ3DNet are open to the readers.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 342-364"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465550","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}

{"title":"SpecColorNet: An interpretable multimodal deep learning approach for predicting SSC of multiple pears","authors":"Xin Xu ,&nbsp;Mengfei Yang ,&nbsp;Jie Yang ,&nbsp;Hao Yin ,&nbsp;Yanyu Chen ,&nbsp;Weijie Lan ,&nbsp;Guoxiang Sun ,&nbsp;Xiaochan Wang ,&nbsp;Shaoling Zhang ,&nbsp;Xiaolei Zhang","doi":"10.1016/j.aiia.2025.12.003","DOIUrl":"10.1016/j.aiia.2025.12.003","url":null,"abstract":"<div><div>Visible/near-infrared spectroscopy provides a non-destructive approach for evaluating soluble solids content (SSC) of pears (<em>Pyrus pyrifolia</em> Nakai). However, variations among pear cultivars, especially in pear color, markedly affect spectral reflectance, thereby limiting the cross-cultivar SSC prediction. To address this, we developed SpecColorNet, a multimodal deep learning framework that integrates spectral and peel color data to accurately predict the SSC of six different pear cultivars simultaneously, and interpreted its decision-making mechanism with gradient-weighted class activation mapping++ (Grad-CAM++). The prediction accuracy of the multi-cultivar SpecColorNet was improved by 9.42 % compared to the multi-cultivar model based solely on spectral data with corresponding RMSEP values of 0.63, 0.60, 0.92, 0.70, 0.54 and 0.55°Brix for six different pear cultivars. In addition, the prediction accuracy of SpecColorNet was comparable to that of single-cultivar spectral models. The interpretation analysis indicated that the SpecColorNet successfully directed the model's attention to the 555–640 nm spectral region, where the most marked cross-cultivar differences occur, thereby improving its generalization and accuracy. Overall, this study proposed a multimodal approach for robust SSC prediction across diverse pear cultivars, which overcame the challenge of cultivar diversity and peel color variability to enable multi-cultivar models with enhanced robustness over pure spectroscopy-based methods.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 619-629"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924241","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}

{"title":"Advancing UAV-based wheat phenology monitoring: A dual-mode framework integrating time-series reconstruction, noise augmentation, and deep learning for robust BBCH estimation","authors":"Ziheng Feng ,&nbsp;Ziya Zhao ,&nbsp;Liunan Suo ,&nbsp;Huiling Long ,&nbsp;Hao Yang ,&nbsp;Xiaoyu Song ,&nbsp;Haikuan Feng ,&nbsp;Bo Xu ,&nbsp;Xinming Ma ,&nbsp;Wei Feng","doi":"10.1016/j.aiia.2025.10.008","DOIUrl":"10.1016/j.aiia.2025.10.008","url":null,"abstract":"<div><div>Precise monitoring of wheat phenology (BBCH scale) is essential for agricultural optimization, yet UAV-based single-phase monitoring encounters spectral ambiguities where multiple vegetation indices correspond to identical growth stages. A dual-mode framework integrating time-series reconstruction with hybrid deep learning was developed to resolve this limitation. UAV multispectral and digital imagery (333 plots, 2023–2024) enabled reconstruction of daily-resolved vegetation indices, color/texture features, and BBCH stages using Gaussian, PCHIP, and linear fitting to mitigate environmental noise. Synthetic datasets incorporating Gaussian noise (5–100 % relative intensity) simulated field variability. Feature selection was optimized through Competitive Adaptive Reweighted Sampling (CARS) and Variance Inflation Factor (VIF). Hybrid CNN-GRU and CNN-LSTM architectures surpassed standalone networks by resolving spectral ambiguities in single-phase data and leveraging temporal patterns during time-series analysis. Time-series models attained maximum accuracy under noise-free conditions (CNN-GRU: R² = 0.90–0.98, RMSE = 3.61–7.65 BBCH units), with accuracy decreasing proportionally to noise intensity. Conversely, single-phase models demonstrated peak performance at 20 % noise intensity (CNN-GRU: R² = 0.56–0.70, RMSE = 15.33–17.22 BBCH units), achieving optimal balance between robustness and practicality for real-time farm monitoring. Extreme noise (100 %) distorted feature distributions (7.25–8.73× expansion), validating controlled augmentation. A novel <em>Rate of Phenological Development (RPDW)</em>—quantified as the slope of BBCH progression—was derived to inform breeding programs, while the noise-optimized single-phase approach enables resource-efficient phenology tracking for family farms. This work bridges methodological innovation (adaptive noise strategies, hybrid architectures) with scalable solutions for precision agriculture, advancing UAV-based phenology monitoring in both academic and applied contexts.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 240-265"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416108","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}

{"title":"Optimizing herbicide reduction: A simulation approach using Artificial Intelligence and different nozzle configurations","authors":"Renato H. Furlanetto ,&nbsp;Ana C. Buzanini ,&nbsp;Arnold W. Schumann ,&nbsp;Nathan S. Boyd","doi":"10.1016/j.aiia.2025.11.011","DOIUrl":"10.1016/j.aiia.2025.11.011","url":null,"abstract":"<div><div>Targeted application aims to minimize product usage by spraying only where needed. However, there is a lack of tools to evaluate the potential savings and the model's limitations. To address this, we developed a computer simulation using two YOLOv8x models (bounding boxes and segmentation masks) to assess spray volume reduction (SVR) and nozzle limitations across varying weed densities. We used Python, OpenCV, and Tkinter to analyze videos from row-middle trials at the University of Florida. The system tracks weeds crossing a horizontal detection line within a 70 cm row-middle width, dividing the frame into one to eight vertical zones representing nozzle distribution. When a weed is detected, the corresponding frame is saved. The system calculates the activation time in seconds by considering the total number of video frames. Spray volume calculations were based on manual measurements of a TeeJet 8001VS nozzle tip, which dispenses a known volume of liquid per second at 35 PSI. The activation time was multiplied by this rate to estimate the targeted spray volume. The broadcast application volume was calculated by multiplying the total video duration by the same tip output. The results showed that the models achieved up to 96 % accuracy (mAP) with no statistical difference. A polynomial model for low and medium weed densities demonstrated SVR of 74 % (six nozzles) and 57 % (seven nozzles). A linear model for high density achieved a 40 % reduction. The lowest reduction occurred with a single nozzle (4 % for medium, 2 % for high density). These findings demonstrate that nozzle density significantly impacted spray reduction at medium and high densities while low-density savings remained consistent.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 592-602"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145747431","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}

{"title":"LPNet: A lightweight progressive network for calyx-aware apple pose estimation in orchard environments","authors":"Wenbei Wang,&nbsp;Licheng Zhu,&nbsp;Bo Zhao,&nbsp;Jianbo Gao,&nbsp;Xuezhi Cui,&nbsp;Zhenhao Han,&nbsp;Siyuan Zhao","doi":"10.1016/j.aiia.2025.12.005","DOIUrl":"10.1016/j.aiia.2025.12.005","url":null,"abstract":"<div><div>Robotic apple harvesting has gradually become a critical requirement for modern agriculture. However, complex orchard environments and limited computational resources pose significant challenges for achieving accurate and efficient vision-based pose estimation of apples. To address these challenges, this study proposes LPNet, a lightweight network tailored for calyx-aware apple pose estimation. LPNet introduces a Calyx-Aware Labeling Strategy (CALS) to improve annotation quality, employs an improved ShuffleNetV2 backbone with progressive channel expansion, and integrates Content-Aware ReAssembly of FEatures (CARAFE) with Bi-directional Feature Pyramid Network (BiFPN) in the neck, enabling compact yet expressive multi-scale feature processing. Furthermore, an Axis-Aligned Soft Geometric Constraint (ASGC) is incorporated to reinforce spatial symmetry and training stability through geometry-aware learning. The head predicts five calyx-centric keypoints, which are then processed through a geometric solver to determine the apple's 2D orientation for harvesting guidance. Experimental results show that LPNet achieves 93.6 % [email protected] at a low computational cost of only 22.2 GFLOPs while maintaining a high inference rate of 158.7 FPS, outperforming representative models such as YOLOv12m-pose, HRFormer, and RTMPose. These results demonstrate that LPNet achieves an effective trade-off between accuracy and efficiency, laying a solid foundation for the future development of practical vision systems in autonomous apple harvesting robots.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 672-694"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924242","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}