Smart agricultural technology最新文献

{"title":"Weed detection based on deep learning from UAV imagery: A review","authors":"Lucía Sandoval-Pillajo ,&nbsp;Iván García-Santillán ,&nbsp;Marco Pusdá-Chulde ,&nbsp;Adriana Giret","doi":"10.1016/j.atech.2025.101147","DOIUrl":"10.1016/j.atech.2025.101147","url":null,"abstract":"<div><div>Weeds are undesirable plants that compete with crops for essential resources such as light, soil, water, and nutrients. Additionally, they can harbor pests that reduce crop yields. In traditional agriculture, weed control is based on applying pesticides throughout the agricultural field, resulting in soil damage, environmental contamination, damage to farm products, and risks to human health. Precision agriculture (PA) has evolved in recent years thanks to sensors, hardware, software, and innovations in unmanned aerial vehicle (UAV) systems. These systems aim to improve the localized application of chemicals in weed control by using advanced image analysis techniques, computer vision, deep learning (DL), and geo-positioning (GPS) to detect and recognize weeds. This subsequently facilitates the implementation of specific control mechanisms in real environments. Recently, automatic weed detection techniques have been developed using UAV imagery. However, these face a significant challenge due to the morphological similarities between weeds and crops, such as color, shape, and texture, which makes their practical and effective differentiation and implementation difficult. This paper presents a systematic literature review (SLR) based on 77 recent and relevant studies on weed detection and classification in UAV imagery using DL architectures. The analysis focuses on key aspects such as using UAVs and sensors, image acquisition and processing, DL architecture, and evaluation metrics. The review covers publications from 2017 to June 2024 from WoS, Scopus, ScienceDirect, SpringerLink, and IEEE Xplore databases. The results allowed the identification of various limitations, trends, gaps, and opportunities for future research. In general, there is a predominant use of multirotor UAVs, particularly the DJI Phantom with RGB sensors, showing a trend towards the integration of multiple sensors (multispectral, LiDAR) operating at heights of around 10 meters, providing good spatial coverage in data acquisition. Likewise, the rapid development of deep learning architectures has driven CNN models such as ResNet for classification, YOLO for detection, U-Net for semantic segmentation, and Mask R-CNN for weed instance segmentation, with a tendency towards new Transformer-based and hybrid architectures. The most common metrics used to evaluate these models include precision, recall, F1-Score, and mAP.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101147"},"PeriodicalIF":6.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570502","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":"Generalized perception of tree-row with distribution-peak feature in 3D point cloud for various orchards navigation","authors":"Yingxing Jiang ,&nbsp;Wuhao Li ,&nbsp;Jizhan Liu ,&nbsp;Muhammad Mahmood ur Rehman ,&nbsp;Binbin Xie ,&nbsp;Jie Wang","doi":"10.1016/j.atech.2025.101137","DOIUrl":"10.1016/j.atech.2025.101137","url":null,"abstract":"<div><div>Universal navigation is crucial for enhancing the environmental adaptability of agricultural robots, promoting large-scale manufacturing and widespread adoption of hardware, and increasing the utilization rate of agricultural robots. Autonomous navigation perception in orchards faces challenges such as the dense growth of branches and leaves obstructing key features, dynamic environmental changes, and significant structural differences across various orchard types. To achieve the goal of autonomous navigation and perception for agricultural robots across various types of orchards. In this study, we analyzed the relationship between the distribution of tree-row point cloud in LiDAR coordinate space and heading, and extracted a commonality distribution-peak feature across various orchards to broaden the generalization of the tree-row perception. Then, we addressed the impact of interference point clouds, local ground unevenness, and large heading offset on perception, and developed a generalization tree-row perception method based on distribution-peak to achieve inter-row localization task in various orchards. Experiments were conducted to validate the algorithm in several orchards of different types, sizes and seasons. Experiments were performed in multiple orchards of different types and specifications, and the results indicated that the heading mean absolute error (<em>MAE</em>) was from 0.88° to 1.25° and the lateral <em>MAE</em> was from 3.57 cm to 7.99 cm of the generalization tree-row perception method in different orchards, which meet the localization requirements for orchard navigation. This study can offer insights into the generalization of environmental perception for orchard navigation.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101137"},"PeriodicalIF":6.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570504","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":"Bayesian optimization with Optuna for enhanced soil nutrient prediction: a comparative study with genetic algorithm and particle swarm optimization","authors":"Bamidele A. Dada ,&nbsp;Nnamdi I. Nwulu ,&nbsp;Seun O. Olukanmi","doi":"10.1016/j.atech.2025.101136","DOIUrl":"10.1016/j.atech.2025.101136","url":null,"abstract":"<div><div>Optimizing soil nutrient prediction models is important for achieving maximum agricultural output and sustainability while also ensuring effective resource management and environmental protection, as demonstrated by a case study in Johannesburg, South Africa. We implemented machine learning (ML), optimization, geographic information systems, and remote sensing. This research investigates the effectiveness of ML algorithms, including random forest (RF), Adaboost (ADB), gradient boosting (GB), and XGBoost (XGB), when used with high-resolution earth observation data. In addition, it examines 2,000 random surface soil samples, ranging from 0 to 20 cm, that were optimized using genetic algorithms (GA), particle swarm optimization (PSO), and Optuna. We train them with 70 % of the data. The investigation confirms that Optuna-optimized models are at least 13 % more precise than GA and PSO models. The concordance correlation coefficient (CCC), R-squared (R²), and mean absolute percentage error (MAPE) increased, while the root mean squared error (RMSE) and mean absolute error (MAE) decreased. Optuna's tree-structured Parzen estimator (TPE) and pruning algorithms are employed to generate more precise estimates of soil nutrients. The majority of models are reduced, computation is expedited, and hyperparameters are enhanced. In the context of precision agriculture, these developments are directly applicable because they enable data-driven fertiliser management, reduce waste, and increase yields. Improved nutrient prediction is also advantageous from an environmental perspective, as it reduces the need for superfluous fertilizer applications and prevents discharge caused by excess fertilizers. Further research will be conducted on reinforcement learning for adaptive searching, multi-objective optimization, and the facilitation of hyperparameter tuning to develop more precise models for predicting soil nutrients.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101136"},"PeriodicalIF":6.3,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570503","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":"Enhanced multi-scale detection of olive tree crowns in UAV orthophotos using a deep learning architecture","authors":"Youness Hnida ,&nbsp;Mohamed Adnane Mahraz ,&nbsp;Ali Yahyaouy ,&nbsp;Ali Achebour ,&nbsp;Jamal Riffi ,&nbsp;Hamid Tairi","doi":"10.1016/j.atech.2025.101126","DOIUrl":"10.1016/j.atech.2025.101126","url":null,"abstract":"<div><div>Object detection in agriculture is vital for identifying and mapping agricultural areas, especially with the growth of precision farming technologies. Traditional methods for counting and yield estimation are time-consuming and demand significant physical effort, presenting substantial challenges for farmers. The use of drones and artificial intelligence, particularly deep learning, has transformed agricultural monitoring, enabling more accurate and rapid analyses. In this study, we introduce an advanced method for detecting tree crowns, focusing on olive trees in farm environments. Our approach is based on an innovative architecture that incorporates a Cross Stage Partial Network (CSPNet) combined with a Feature Pyramid Network (FPN) and Path Aggregation Network (PAN), augmented by DropBlock regularization. Our architecture is tailored for multi-scale object detection from UAV-captured imagery, addressing issues such as small object detection, complex backgrounds, object rotation, scale variations, and category imbalances in both simple imagery and high-resolution orthophotos. These orthophotos are produced by stitching together multiple high-quality images we captured from various angles and altitudes to create a comprehensive and detailed view of the orchard. Our methodology includes splitting images into different sizes (1 × 1, 3 × 3, 6 × 6, and 9 × 9) to enhance analysis and improve detection performance at various scales. This comprehensive approach has enabled us to conduct an in-depth analysis of olive trees, classified into small, medium, and large sizes. The results demonstrate the robustness of our method in addressing common object detection challenges in agricultural contexts, achieving a precision of 92.47 %, recall of 91.40 %, F1-score of 91.93 %, [email protected] of 94.00 %, and mAP@[0.5:0.95] of 87.00 %. These results confirm its reliability for optimizing precision farming practices, including crop condition monitoring and resource management.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101126"},"PeriodicalIF":6.3,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144570505","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":"Detection of calcium deficiency in indoor-grown lettuce under LED lighting using computer vision","authors":"Zhian Li,&nbsp;Saeed Karimzadeh,&nbsp;Alise Chavanapanit,&nbsp;Ali Moghimi,&nbsp;Md Shamim Ahamed","doi":"10.1016/j.atech.2025.101144","DOIUrl":"10.1016/j.atech.2025.101144","url":null,"abstract":"<div><div>Calcium deficiency and its associated physiological disorders, such as tip burn, pose considerable challenges for indoor hydroponic lettuce production, impacting both yield and quality. This study presents a novel approach combining advanced image segmentation and classification techniques to detect calcium deficiency in lettuce during its growth stages under colored LED lighting. Early detection of nutrient deficiencies is crucial for timely intervention and efficient nutrient management. This experiment involved growing butterhead lettuce plants under a Deep-Water Culture (DWC) system with controlled calcium treatments. Preprocessing techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE) and Red Color Correction (RCC), were applied to enhance quality and consistency and augmented to improve model generalization. Our methodology employed a two-stage process that allowed us to leverage the strengths of specialized models. First, lettuce leaves were segmented from the background using state-of-the-art models, including U-Net, U-Net++, Recurrent U-Net, and Inception U-Net. U-Net++ demonstrated the highest segmentation accuracy (98.56 %) with robust generalization compared to the other models. Segmentation isolates the region of interest and removes background noise, enabling the classifier to focus more effectively on disease-related features. Deep learning classification models, such as ResNet and EfficientNet, were applied in the second stage to detect calcium deficiency from the segmented images. EfficientNetB2 emerged as the most reliable classifier, achieving an accuracy of 91.51 % on the RCC dataset, while Resnet50 achieved a comparable accuracy of 91.18 % on the same dataset. This study highlights the potential of integrating deep learning models into automated hydroponic systems for real-time nutrient monitoring, offering a practical solution to enhance productivity and sustainability in indoor hydroponic farming.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101144"},"PeriodicalIF":6.3,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563509","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":"Improved sap flow prediction: A comparative deep learning study based on LSTM, BiLSTM, LRCN, and GRU with stem diameter data","authors":"Amora Amir ,&nbsp;Marya Butt","doi":"10.1016/j.atech.2025.101105","DOIUrl":"10.1016/j.atech.2025.101105","url":null,"abstract":"<div><div>Introduction Recording sap flow in plants is essential to understanding water usage, especially for herbaceous species like tomatoes. While plant physiology research has progressed, there remains a gap in applying sap flow sensor data to these species. In this study, the predictive capabilities of Recurrent Neural Network (RNN) architectures—LSTM, GRU, BiLSTM, and LRCN—are explored for sap flow estimation in tomato plants using stem diameter variations as the sole input. Unlike existing studies that rely on multi-variable environmental data from large-scale datasets such as SAPFLUXNET, COCO or KAGGLE this research is based on in-house experimental data collected in close collaboration with a sensor developer and farmers. The experimental setup reflects practical conditions relevant to controlled environment agriculture. To the best of the authors’ knowledge, this is the first study to investigate the potential of RRN deep learning models to infer sap flow directly from stem diameter signals in tomato plants. A comprehensive performance comparison of the models is presented under varying input time windows, with a discussion on implications for real-time irrigation and plant monitoring solutions. Deep learning models were designed using four advanced Recurrent Neural Networks (RNN) architectures: LSTM, BiLSTM, LRCN, and GRU, trained with past sap flow and stem diameter data from tomato plants. Based on the last three hours of data, the models predicted sap flow for the next hour. Metrics like Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R² were used to evaluate performance, and early stopping was applied to prevent overfitting during training. The LSTM model achieved the lowest RMSE, excelling at short-term sap flow prediction. However, both BiLSTM and GRU models performed well overall, particularly in capturing, more significant fluctuations and peaks. R² 0.83 values across all models were around 7.2, with MAE values below 5.8, demonstrating robust predictive potential. These results suggest that advanced deep learning models, particularly BiLSTM, can significantly improve the prediction of plant sap flow, enhancing efficiency in water management in precision agriculture. Future research could apply these models to other herbaceous species.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101105"},"PeriodicalIF":6.3,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548890","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":"Enhanced LSTM-based AI model for accurate dissolved oxygen prediction in aquaculture systems","authors":"Ala Saleh Alluhaidan ,&nbsp;Prabu P ,&nbsp;Romana Aziz ,&nbsp;Shakila Basheer","doi":"10.1016/j.atech.2025.101140","DOIUrl":"10.1016/j.atech.2025.101140","url":null,"abstract":"<div><div>Accurate monitoring and prediction of dissolved oxygen (DO) levels in aquaculture systems are crucial for maintaining optimal water quality and ensuring fish health. This study presents an enhanced Long Short-Term Memory (LSTM)-based model for DO prediction, leveraging historical data on DO levels, water temperature, and other environmental parameters. Unlike traditional methods that rely on fixed assumptions, the proposed model dynamically adapts to changing environmental conditions, offering real-time, high-precision forecasts. Experimental results demonstrate that the enhanced LSTM model achieves a prediction accuracy of 92.28 %, outperforming existing models such as IFP (81.97 %), DOE (63.81 %), and DOP (86.79 %)<strong>.</strong> The model’s superior accuracy and adaptability make it a reliable tool for aquaculture management, helping to optimize DO levels and reduce the risk of fish mortality. By integrating artificial intelligence into aquaculture monitoring, this approach contributes to improved system productivity and sustainability.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101140"},"PeriodicalIF":6.3,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518317","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":"An interpretable integrated machine learning framework for genomic selection","authors":"Jinbu Wang ,&nbsp;Jia Zhang ,&nbsp;Wenjie Hao ,&nbsp;Wencheng Zong ,&nbsp;Mang Liang ,&nbsp;Fuping Zhao ,&nbsp;Longchao Zhang ,&nbsp;Lixian Wang ,&nbsp;Huijiang Gao ,&nbsp;Ligang Wang","doi":"10.1016/j.atech.2025.101138","DOIUrl":"10.1016/j.atech.2025.101138","url":null,"abstract":"<div><div>Although machine learning (ML) methods have shown growing promise for genomic selection (GS), several key challenges hinder their widespread application. In this study, we conducted a comprehensive analysis comparing the performance of various ML models, along with investigations into parameter optimization, dimensionality reduction, feature selection, and the “black box” problem. We also proposed an efficient and interpretable framework, NTLS (NuSVR + TPE + LightGBM + SHAP). In the prediction of Yorkshire pig populations, NTLS outperformed the genomic best linear unbiased prediction (GBLUP) model, achieving improvements in predictive accuracy of 5.1%, 3.4%, and 1.3% for days to 100 kg (DAYS), back fat at 100 kg (BF), and number of piglets born alive (NBA), respectively. Moreover, we introduced the NuSVR model, which achieved the highest accuracy among nine compared algorithms. Our findings further highlight the importance of interpretable learning in GS and provide a detailed multi-level application of the SHAP algorithm.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101138"},"PeriodicalIF":6.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563460","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":"A bio-inspired framework for apple leaf disease detection: Integrating lesion localization, ant colony optimization, and machine learning","authors":"Xiaolong Li ,&nbsp;Feifan Huang ,&nbsp;Haotian Sun ,&nbsp;Jiayu He ,&nbsp;Seyed Mohamad Javidan ,&nbsp;Yiannis Ampatzidis ,&nbsp;Zhao Zhang","doi":"10.1016/j.atech.2025.101141","DOIUrl":"10.1016/j.atech.2025.101141","url":null,"abstract":"<div><div>Apple trees, among the most widely cultivated and economically important orchard species, are highly susceptible to foliar diseases such as Black Spot, Black Rot, and Cedar Rust. Due to the visual similarity of symptoms, accurately distinguishing among these diseases poses a major challenge. Conventional diagnostic approaches, such as expert visual assessments and laboratory analyses, are often time-consuming, costly, and limited to post-symptomatic stages. To address the growing need for rapid, accurate, and scalable solutions in precision disease detection and management, this study presents a novel framework integrating image processing, artificial intelligence (AI), and ant colony optimization (ACO) for automated disease classification in apple leaves. The proposed method comprises five key steps: (1) background removal from leaf images, (2) diseased area detection, (3) extraction of texture, color, and shape features, (4) feature selection using ACO to identify the most informative attributes, and (5) disease classification using a support vector machine (SVM) classifier. Experimental results demonstrate that preprocessing steps, particularly background removal and lesion localization, significantly enhance classification accuracy. The system achieved class-wise accuracies of 95.12 % (Black Spot), 90.91 % (Black Rot), 94.87 % (Cedar Rust), and 88.89 % (Healthy), with an overall classification accuracy of 92.50 %. Among the features used, texture contributed most significantly to performance, followed by color and shape. These findings highlight the effectiveness of combining diverse image features with bio-inspired optimization techniques for plant disease detection and offer a promising direction for future research and deployment in intelligent agricultural monitoring systems.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101141"},"PeriodicalIF":6.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518316","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 smart aquaculture: Cost-efficient strategies for climbing perch cultivation using AI-based models","authors":"Kosit Sriputhorn ,&nbsp;Achara Jutagate ,&nbsp;Surasak Matitopanum ,&nbsp;Rungwasun Kraiklang ,&nbsp;Rapeepan Pitakaso ,&nbsp;Chakat Chueadee ,&nbsp;Sarayut Gonwirat","doi":"10.1016/j.atech.2025.101108","DOIUrl":"10.1016/j.atech.2025.101108","url":null,"abstract":"<div><div>This study introduces a hybrid AI-based optimization framework to enhance climbing perch aquaculture in smart farming systems, targeting improvements in both productivity and cost-efficiency. By integrating Taguchi experimental design with reinforcement learning and metaheuristic algorithms, the model identifies critical environmental and operational parameters that influence fish growth and economic outcomes. A multi-objective regression approach is applied to optimize key factors including water temperature, dissolved oxygen, pH, feeding schedules, and stocking densities. The RL-AMIS method, which combines Pareto front analysis and TOPSIS, successfully balances trade-offs between cost and productivity. Experimental results show a 15.3 % reduction in operational costs and a 17.8 % improvement in growth efficiency compared to conventional methods. These findings demonstrate that AI-driven optimization can enhance scalability and adaptability in aquaculture systems, supporting sustainable production strategies.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"12 ","pages":"Article 101108"},"PeriodicalIF":6.3,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563510","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}