Information Processing in Agriculture最新文献

{"title":"Data governance in smart farming: A systematic review of challenges and gaps","authors":"Radwan Rouzky ,&nbsp;Abdolhossein Sarrafzadeh ,&nbsp;Evelyn Sowells-Boone ,&nbsp;Hannaneh B. Pasandi ,&nbsp;Gregory Goins","doi":"10.1016/j.inpa.2025.11.006","DOIUrl":"10.1016/j.inpa.2025.11.006","url":null,"abstract":"<div><div>Smart Farming (SF) has emerged as a transformative approach in modern agriculture, leveraging advanced technologies to meet escalating global food demands. However, the extensive use of digital devices generates vast amounts of data, necessitating robust governance frameworks to ensure secure, ethical, and efficient management. This systematic review evaluates SF data governance literature from 2018 to 2024, analyzing 32 studies that collectively highlight critical challenges and opportunities. Key findings include the absence of standardized governance frameworks, limited exploration of security mechanisms, and insufficient farmer’s awareness regarding data privacy and security. Our novel taxonomy categorizes SF governance into data security, privacy, and sharing frameworks, offering actionable insights for stakeholders. By identifying gaps—such as the need for transparent governance systems and comprehensive regulatory policies—this paper provides a roadmap for advancing ethical and effective data governance in agriculture.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 303-317"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060319","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":"ICIS: Intelligent and Label-Free crop identifying using Multi-Source time series and model transfer","authors":"Su Rina ,&nbsp;Wu Nile ,&nbsp;Na Mula ,&nbsp;Sicheng Wei ,&nbsp;Ying Guo ,&nbsp;Jiquan Zhang ,&nbsp;Zhijun Tong ,&nbsp;Xingpeng Liu ,&nbsp;Chunli Zhao ,&nbsp;Cha Ersi","doi":"10.1016/j.inpa.2025.11.005","DOIUrl":"10.1016/j.inpa.2025.11.005","url":null,"abstract":"<div><div>Crop cultivation is intrinsically linked to human well-being, and the timely identification of crop types is critical for agricultural monitoring and food security assessment. However, without extensive field surveys, achieving timely, large-scale, high-resolution crop identification remains challenging. This study proposes an intelligent crop identification strategy (ICIS) that integrates crop phenological development information with a model transfer strategy based on machine learning, generating 10-meter resolution crop distribution maps of major crop types from 2020 to 2023 in Northeast China (NE). ICIS utilizes time series data from Sentinel-1 (S1) and Sentinel-2 (S2) to extract multi-source temporal features covering the full growth cycles of maize, rice, and soybeans. A Random Forest (RF) classifier was trained on representative sample sites, and model transfer was employed to predict crop distribution across different years and regions. The experimental results show that the models achieved F1-scores above 0.92 across provinces, peaking at 0.97. For rice, the F1-scores for Jilin, Heilongjiang, and Liaoning remained stable at 0.94. Maize F1-scores consistently exceeded 0.92, and although the soybean accuracy varied spatially, it reached 0.97 in certain areas. Comparison with crop area statistics from the China Statistical Yearbook at the city level revealed that the average root mean square error (RMSE) across four years for the three crops was 8.6 × 10⁴ ha, with coefficients of determination (R²) consistently above 0.90, reaching up to 0.96. These results demonstrated the strong generalizability and robustness of the proposed ICIS Strategy for crop mapping across both temporal and spatial scales. Moreover, this study reveals the complementary strengths and key contributions of optical and Synthetic Aperture Radar (SAR) time series features in modeling crop growth dynamics. Overall, the ICIS provides an efficient and reliable technical means for the rapidly acquiring large-scale crop information. It provides robust support for agricultural monitoring and food security assessment, demonstrates significant potential for large-scale agricultural applications, and offers valuable insights and technical support for remote sensing data processing, image analysis, and machine learning-based agricultural automation and decision-making.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 283-302"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060320","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":"YOLO-MEST: A re-parameterized multi-scale fusion model with enhanced detection head for high-accuracy tea bud detection","authors":"Chuanyang Yu ,&nbsp;Yi Xue ,&nbsp;Liuyang Zhang ,&nbsp;Xue An ,&nbsp;Ce Liu ,&nbsp;Liqing Chen","doi":"10.1016/j.inpa.2025.10.001","DOIUrl":"10.1016/j.inpa.2025.10.001","url":null,"abstract":"<div><div>Accurate recognition of tea buds is essential for automated harvesting. However, conventional image-based methods have difficulty with complex field conditions, such as varying illumination, occlusion, and cluttered backgrounds. To overcome these challenges, we introduce YOLO-MEST, a new detection model that incorporates multi-scale feature fusion. This model introduced the RepNCSELAN4 module to enhance feature extraction capabilities and the SPPELAN module to improve feature fusion. The efficient detection head, LiteShiftHead, was added to the network output to improve the accuracy of bounding boxes and classification regression. An improved loss function dDIoU based on the difference in the width-to-height ratio of bounding boxes was designed to enhance the accuracy of bounding box localization further. To form a complete detection-to-picking pipeline, a morphological algorithm-based tea bud picking point estimation algorithm was further proposed, which effectively determined the tea bud picking points based on the detection results. We conducted performance tests on tea bud recognition based on a self-built dataset of high-quality tea. Compared to the original YOLOv8 model, the YOLO-MEST model increased mAP50 by 1.7% and mAP by 4.4%, respectively. Ablation studies confirm the contribution of each component. The proposed method significantly improves detection accuracy and supports practical intelligent tea harvesting.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 163-175"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060340","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":"Evaluation of irrigation doses and planting density of hemp (Cannabis sativa, L.) in a temperate climate through CRITIC-TOPSIS-OWA","authors":"J.M. Cámara-Zapata ,&nbsp;S. Pardo-Pina ,&nbsp;R. Abadía ,&nbsp;J.M. Brotons-Martínez","doi":"10.1016/j.inpa.2025.11.003","DOIUrl":"10.1016/j.inpa.2025.11.003","url":null,"abstract":"<div><div>Hemp is a high interest crop in the agri-food sector due to its large number of applications and its beneficial effects. However, there are no clear recommendations to optimize its production, perhaps due to its strict regulation. In order to optimize the management of the crop, the present work analyzes the effect of irrigation doses and planting density on the growth, production, and quality of the Kompolti variety of hemp, with an irrigation dose equal to the total water requirements of the plant and two deficit treatments, corresponding to 75 and 50 % of these requirements, and two planting densities, 7.0 and 3.1 plants m⁻². For this, a multi-criteria analysis was performed that proposes the use of the Ordered Weighted Averaging (OWA) operator combined with the CRiteria Importance Through Inter-criteria Correlation (CRITIC) to obtain the weights, where the correlation is obtained as a function of the Pearson-OWA, to determine the aggregation of the quadratic differences by assigning different levels of importance as a function of their size. This application allows for the objective estimation of the weights starting with the attitudinal character of the decisor and its integration to the TOPSIS method. The strength of the innovation proposal has been evaluated, demonstrating that it does not exhibit a rank reversal phenomenon. Furthermore, the resulting ranking has been compared with those obtained using direct assignment methods, such as TOPSIS itself, Multicriteria Optimization and Compromise Solution (VIKOR), and Simple Additive Weighting (SAW), revealing minimal differences. This allows us to regard the proposed method as a generalization of these approaches. In the conditions utilized, the irrigation dose had a greater effect on growth, production, and the content of long fibers, rather than the planting density. In turn, planting density had a greater influence on the cannabidiol (CBD) content as compared to the irrigation dose. The classification obtained was led by the treatments with a higher planting density. Among them, the decrease in the irrigation dose worsened the assessment of the treatments. However, when the planting dose is low, a moderate water deficit improves the results with respect to the complete irrigation treatment or with intense deficit irrigation.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 253-262"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060317","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":"Prediction of greenhouse temperature and humidity across growing seasons: Hybridization of process-based model and deep neural networks","authors":"Kaige Liu ,&nbsp;Tao Ji ,&nbsp;Ming Li ,&nbsp;Xinting Yang ,&nbsp;Junli Sun ,&nbsp;Huiying Liu ,&nbsp;Ran Liu","doi":"10.1016/j.inpa.2025.11.002","DOIUrl":"10.1016/j.inpa.2025.11.002","url":null,"abstract":"<div><div>Accurate prediction and precise management of greenhouse environments contribute to improving crop yield and quality. However, conventional process-based models exhibit inherent recalibration constraints due to their fixed parameterization schemes, particularly when confronting nonlinear dynamics in cross-seasonal greenhouse environments. This study presents a novel hybrid mechanistic model capable of dynamic parameter calibration for multi-season applications. These dynamic parameters in the mechanistic model were adaptively predicted by deep neural networks. The greenhouse model calibration results showed that the errors for indoor air temperature and relative humidity were <em>RMSE</em> = 1.6104 °C, 6.9379 %, <em>MAE</em> = 1.0463 °C, 4.3797 %, <em>R²</em> = 0.9090, 0.8290, and <em>PBIAS</em> = −1.3807 %, −0.0005 %, respectively. Finally, in the absence of indoor environmental reference data, the hybrid greenhouse model with adaptive parameters was validated from September 5, 2024, to November 25, 2024. The validation involves two approaches: one using measured weather data as inputs, and the other utilizing a public weather forecast API. The validation results indicate robust model performance regardless of the input data source, including both measured weather data and weather forecast data. This study develops a robust tool for forecasting greenhouse microclimates throughout growing seasons, thereby enabling optimized environmental management.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 238-252"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060338","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":"Recent advances in computer vision for non-contact phenotyping and weight estimation in livestock: A systematic review","authors":"Jitong Xu,&nbsp;Wei Jiang,&nbsp;Liangju Wang,&nbsp;Hongying Wang,&nbsp;Junhua Wu,&nbsp;Yang Shen,&nbsp;Chengtian Zhu,&nbsp;Shuaihua Hao,&nbsp;Cailing Liu","doi":"10.1016/j.inpa.2025.10.003","DOIUrl":"10.1016/j.inpa.2025.10.003","url":null,"abstract":"<div><div>With the advancement of Precision Livestock Farming (PLF), traditional manual measurement methods for livestock phenotypic trait are seriously limited in meeting modern demands in terms of accuracy, efficiency and animal welfare. In response, computer vision (CV)-based non-contact measurement techniques, especially those integrated with machine learning (ML) and deep learning (DL), have demonstrated significant potential in livestock body size and weight estimation. This study presents a systematic review of recent progress in 2D image-based, 3D point cloud-based, and fusion-based CV methods for phenotypic measurement of cattle, pigs, and sheep. The comparative analysis covers core algorithmic frameworks, sensor types, pose normalization techniques, and regression model performance. 2D methods offer low-cost deployment with typical measurement error ranging from 5 % to 8 %, while 3D techniques reduce error to 3 % to 5 % and achieve R² values above 0.95. DL-based models can further enhance robustness, especially in dynamic and occluded farm environments. Other than the advances above, key challenges still exist, including low multi-view point cloud registration and model generalization across scenarios, and the scarcity of labeled data. Addressing these disadvantages requires improved multi-modal data fusion, large-scale model transfer strategies, the development of lightweight model design, and real-time deployable architectures. This review provides a structured technical reference for researchers and engineers, especially those new to the field, with insights into end-to-end workflows and expects future innovations that can support intelligent, non-invasive, and large-scale livestock management systems.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 192-213"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060314","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":"YOLOv9-SCRep: A lightweight instance segmentation and counting model for dense rice panicle images","authors":"Xin Tian ,&nbsp;Junshuo Wei ,&nbsp;Zhenran Gao ,&nbsp;Quanzhi Zhao","doi":"10.1016/j.inpa.2025.11.004","DOIUrl":"10.1016/j.inpa.2025.11.004","url":null,"abstract":"<div><div>Accurate instance segmentation and counting of rice panicles are essential for evaluating crop growth, optimizing field management, and improving yield prediction. However, challenges such as variable lighting, complex backgrounds, and overlapping panicles often hinder reliable detection. To address these issues, we propose YOLOv9-SCRep, an enhanced YOLOv9-based instance segmentation model specifically designed for precise segmentation and counting of rice panicles. This model incorporates four key innovations: (1) C2f-SCConv modules replace conventional convolution layers in the backbone, improving feature extraction efficiency and minimizing information loss; (2) RepNCSPELAN4AKConv modules in the detection head enhance representational capacity and multi-scale adaptability; (3) Varifocal Loss mitigates class imbalance, boosting classification accuracy; and (4) Inner-IoU refines bounding-box regression, ensuring greater localization precision. Experimental results show that YOLOv9-SCRep achieves 97.4 % precision, 97.7 % recall, 99.1 % mAP@50, and 97.5 % F1-score, while maintaining a low inference time of just 10.1 ms per image. Compared to mainstream models like YOLOv9 and SOLOv2, our approach offers significant improvements in both accuracy and efficiency, effectively reducing missed and duplicate detections. This ensures robust segmentation and stable counting in challenging field conditions, providing a reliable solution for monitoring rice growth and predicting yield.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 263-282"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060318","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":"Precise path planning based on improved A* and DWA algorithms for complex agricultural environments","authors":"Zhiqiang Yu,&nbsp;Derun Cui,&nbsp;Yuzheng Zhu,&nbsp;Kai Zhou,&nbsp;Zhilong Zhang,&nbsp;Yuhua Li","doi":"10.1016/j.inpa.2025.11.001","DOIUrl":"10.1016/j.inpa.2025.11.001","url":null,"abstract":"<div><div>This study introduces a hybrid algorithm combining an enhanced A* algorithm with the Dynamic Window Approach (DWA) to improve agricultural robot navigation. The A* algorithm incorporates a Grey Wolf Optimizer (GWO) for dynamic heuristic weighting based on obstacle density and a bidirectional search strategy to boost efficiency. Path smoothing is achieved via key point selection and third-order B-spline fitting. In DWA, a Q-Learning mechanism adaptively optimizes weight coefficients, and a posture adjustment function eliminates initial heading deviations to avoid redundant steering. Simulations reveal that in simple environments, the proposed method reduces the computation time by 19.1 % and increases the robot speed by 24.2 %, with only a 1.7 % increase in path length. In complex settings, it reduces the execution time by 20.1 %, shortens the path length by 1.2 %, and raises the speed by 25 %. Tests in a greenhouse demonstrate effective navigation in single- and multi-row operations, with 19.3 % and 37.7 % lower distance deviations, and 8.7 % and 14.6 % lower heading deviations, respectively. The experimental results demonstrate that the fusion algorithm, by simultaneously optimizing global and local path planning, significantly enhances the navigation accuracy, operational efficiency, and environmental adaptability of agricultural robots, thereby meeting the critical requirements for intelligent navigation in precision agriculture.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 214-237"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060337","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":"RPFbot: Unsupervised modular robot for precise feeding of rabbits","authors":"Wei Jiang ,&nbsp;Hongming Tang ,&nbsp;Yinghe Qin ,&nbsp;Jitong Xu ,&nbsp;Daobilige Su ,&nbsp;Liangju Wang ,&nbsp;Hongying Wang","doi":"10.1016/j.inpa.2025.10.002","DOIUrl":"10.1016/j.inpa.2025.10.002","url":null,"abstract":"<div><div>In China’s efforts to implement the “Big Food Concept” for food security, the rapid development of the rabbit farming industry has become a key part of the livestock sector. Feeding systems are transitioning from traditional restrictive methods to precision feeding, with intelligent feeding equipment now a central trend in the industry. This study focuses on large-scale rabbit farming. The objective of the study was to develop the the Rabbit Precision Feeding Robot (RPFbot), which integrates mobile robot, precision feeding, robotic arm, and visual servoing technologies to overcome key challenges in rabbit feeding. RPFbot autonomously performs timed, differentiated, and precise feeding tasks, with feed quantification errors controlled to within 1.73 %. Field tests show excellent precision: the average error (unit: m) for the x-axis is 0.01295, the y-axis 0.00878, and the z-axis 0.00603. The Euclidean and Manhattan distance errors (unit: m) are 0.01896 and 0.02776, respectively, and the average joint error is 0.045337 rad, with a feeding success rate of 100 %. In terms of feeding efficiency, the average feeding times for the three rabbit cage feed boxes troughs corresponding to a single feeding point were 25 s, 23 s, and 21 s, respectively. RPFbot effectively prevents feed fragmentation, cross-contamination, and rabbit stress, while enabling differentiated feeding for rabbits within the same batch. This not only improves feeding efficiency but also supports optimized breeding, advancing the shift from automated to unmanned intelligent farms. The robot plays a crucial role in sustainable development and cost efficiency.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 176-191"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060341","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":"Data mining algorithms for the prediction of inorganic nitrogen in water: Mechanisms, techniques, and applications","authors":"Jiaran Zhang ,&nbsp;Chuang Tang ,&nbsp;Liang Wang ,&nbsp;Chuyuan Wei","doi":"10.1016/j.inpa.2025.12.005","DOIUrl":"10.1016/j.inpa.2025.12.005","url":null,"abstract":"<div><div>Accurate prediction of inorganic nitrogen concentrations is critical for assessing water quality and supporting sustainable aquatic ecosystem management. Data mining algorithms have demonstrated significant potential in inorganic nitrogen prediction. This review provides a systematic examination of data mining algorithms applied to inorganic nitrogen prediction for aquatic environments, focusing on the complete modeling pipeline from data preparation to predictive algorithm deployment. Essential data preparation techniques involving data acquisition, data cleaning, dimensionality reduction, and dataset partitioning are described in detail. Then, the mechanisms, techniques, and applications are critically evaluated according to the following five categories of data mining algorithms: machine learning models, deep learning models, tree-based models, fuzzy neural networks, and hybrid models. FNN and deep learning models yield top-tier accuracy (mean R² &gt; 0.90). In addition, hybrid models integrating LSTM have emerged as the most promising and prevalent framework, driving advancements in prediction precision. Despite significant progress, the field continues to face challenges such as insufficient data quality, limited model generalizability, and a lack of interpretability. Integrating multimodal data, developing lightweight real-time prediction systems, combining explainable artificial intelligence with physics-informed constraints, and strengthening uncertainty quantification will strongly advance inorganic nitrogen prediction models toward more accurate, reliable, and practical decision-support tools. This review provides a systematic reference for researchers and practitioners.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"13 2","pages":"Pages 318-342"},"PeriodicalIF":7.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148060321","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}