Smart agricultural technology最新文献

{"title":"Design and development of an IoT-based dendrometer system for real-time trunk diameter monitoring of Christmas trees","authors":"Thomas Rose,&nbsp;Nawab Ali,&nbsp;Younsuk Dong","doi":"10.1016/j.atech.2024.100765","DOIUrl":"10.1016/j.atech.2024.100765","url":null,"abstract":"<div><div>Real-time assessment of trunk growth is vital for understanding tree growth fluctuation, especially under irrigation application and other environmental factors. Accurate trunk diameter assessment is crucial for optimizing water use and tree health improvement, and its cost-effectiveness is needed for widespread adoption in agriculture. This study focused on the development of an accurate and low-cost IoT-based dendrometer system for real-time trunk diameter measurement of Christmas trees. The dendrometer sensor was calibrated (R² = 0.99) to ensure the accurate conversion of sensor voltage to trunk diameter fluctuations. This IoT-based dendrometer system consists of a platform that enables wireless data transmission, cloud-based storage and real-time analysis of the trunk diameter. Temperature fluctuation influenced the sensor readings with no impact, which validated the system's reliability in open field conditions. Christmas tree diameter monitoring showed significant trunk expansion and contraction under irrigation application and water stress, respectively, which signifies the system ability to monitor the real-time trunk growth responses. Cost analysis makes this technology economical and reliable for widespread application in precision agriculture. Therefore, this low-cost IoT-based dendrometer system is reliable, accurate, and economically viable for improving irrigation management, tree health monitoring, and supporting farmers through data-driven agricultural practices.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100765"},"PeriodicalIF":6.3,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181123","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}

{"title":"Configuration of a multisensor platform for advanced plant phenotyping and disease detection: Case study on Cercospora leaf spot in sugar beet","authors":"Facundo R. Ispizua Yamati ,&nbsp;Jonas Bömer ,&nbsp;Niklas Noack ,&nbsp;Thomas Linkugel ,&nbsp;Stefan Paulus ,&nbsp;Anne-Katrin Mahlein","doi":"10.1016/j.atech.2024.100740","DOIUrl":"10.1016/j.atech.2024.100740","url":null,"abstract":"<div><div>Plant phenotyping, which involves measuring and analysing plant traits, has seen significant advances in recent years by integrating autonomous platforms and sophisticated sensor systems. In contrast to traditional methods, modern unmanned ground vehicles (UGVs) provide robust and accurate phenotyping capabilities by enabling close, detailed and continuous monitoring of crops under different environmental conditions. This study presents the configuration and validation of a multi-sensor platform (MSP) integrated with a UGV to improve plant phenotyping through advanced data fusion and co-registration techniques. The platform incorporates red, green, and blue channel (RGB), hyperspectral from visible light (VIS) and near-infrared light (NIR) spectrum, thermal sensors, and a three-dimensional (3D) light detection and ranging (LiDAR), all subjected to extensive calibration to ensure precise temporal and spatial alignment. Intrinsic calibration was applied, including correcting the spectral signatures of VIS and NIR. Additionally, timestamps were synchronised using the VIS sensor as the primary reference due to its central position and higher data acquisition frequency. Homography matrices were computed using checkerboard patterns for geometric alignment across sensors, and motion corrections accounted for UGV movement and ground sample distance. LiDAR point clouds were transformed into depth-maps (DMs) using radial basis function interpolation, enriching the spatial data for further analysis. The co-registered and synchronised MSP was tested for detecting Cercospora leaf spot (CLS) in sugar beet plants during a field experiment. Two models were implemented: (1) a soil and plant segmentation model based on the DeepLabV3+ architecture, achieving an F1-score of 0.85 and an accuracy of 0.95, and (2) a CLS severity scoring model using a custom convolutional neural network (CNN). The severity model, leveraging NIR and DM channels, achieved an F1-score of 0.7066, accuracy of 0.7104, and recall of 0.7167, with NIR wavelengths between 814<!--> <figure><img></figure> and 851<!--> <figure><img></figure> contributing significantly to performance. These results highlight the importance of accurate data fusion and synchronisation in multi-sensor systems for plant phenotyping. Moreover, the study demonstrates the potential of integrating multiple sensors on a UGV for precision agriculture, thereby enhancing MSP effectiveness in crop monitoring and disease detection.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100740"},"PeriodicalIF":6.3,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181128","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}

{"title":"Reinforcement learning-based model predictive control for greenhouse climate control","authors":"Samuel Mallick ,&nbsp;Filippo Airaldi ,&nbsp;Azita Dabiri ,&nbsp;Congcong Sun ,&nbsp;Bart De Schutter","doi":"10.1016/j.atech.2024.100751","DOIUrl":"10.1016/j.atech.2024.100751","url":null,"abstract":"<div><div>Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while enforcing physical constraints. However, prediction models for greenhouse systems are inherently inaccurate due to the complexity of the real system and the uncertainty in predicted weather profiles. For model-based control approaches such as MPC, this can degrade performance and lead to constraint violations. Existing approaches address uncertainty in the prediction model with robust or stochastic MPC methodology; however, these necessarily reduce crop yield due to conservatism and often bear higher computational loads. In contrast, learning-based control approaches, such as reinforcement learning (RL), can handle uncertainty naturally by leveraging data to improve performance. This work proposes an MPC-based RL control framework to optimize the climate control performance in the presence of prediction uncertainty. The approach employs a parametrized MPC scheme that learns directly from data, in an online fashion, the parametrization of the constraints, prediction model, and optimization cost that minimizes constraint violations and maximizes climate control performance. Simulations show that the approach can learn an MPC controller that significantly outperforms the current state-of-the-art in terms of constraint violations and efficient crop growth.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100751"},"PeriodicalIF":6.3,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181131","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}

{"title":"Deep learning driven methodology for the prediction of mushroom moisture content using a novel LED-based portable hyperspectral imaging system","authors":"Kai Yang,&nbsp;Ming Zhao,&nbsp;Dimitrios Argyropoulos","doi":"10.1016/j.atech.2024.100747","DOIUrl":"10.1016/j.atech.2024.100747","url":null,"abstract":"<div><div>This study proposes a deep-learning driven methodology for the analysis of mushroom moisture content (MC) datasets acquired using a novel portable hyperspectral imaging (HSI) system. One-dimensional convolutional neural network (1D-CNN) was developed and validated to process the raw HSI data of white button mushrooms <em>(Agaricus bisporus)</em> for MC prediction. For comparison purposes, state-of-the-art machine learning algorithms, i.e., support vector machine regression (SVMR) and partial least squares regression (PLSR) were also investigated for the model development based on five spectra pre-processed methods using two different lighting systems i.e., enhanced light-emitting diode (LED) and tungsten halogen (TH). Overall, the predictive models based on the HSI data acquired using the LED lights (R_p² of 0.977, RMSEP of 4.27 %, and RPD_p of 6.89) exhibited better performances on the prediction of mushroom MC than those models developed using the TH-HSI data (R_p² of 0.868, RMSEP of 10.69 %, and RPD_p of 2.75). Specifically, the 1D-CNN model based on the raw LED-HSI data (R_p² of 0.972, RMSEP of 4.70 % and RPD_p of 6.29) and the SVMR model based on multiplicative scatter correction (MSC) pretreated LED-HSI data (R_p² of 0.977, RMSEP of 4.27 %, and RPD_p of 6.89) achieved exceptional predictive accuracy for mushroom MC. This finding highlights the effectiveness of the 1D-CNN model in the analysis of HSI data, which performed similarly to the SVMR model without requiring complex data preprocessing steps. In addition, the feasibility of employing a novel LED illumination system in conjunction with a portable HSI camera for the precise MC monitoring of button mushrooms was demonstrated in the present work.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100747"},"PeriodicalIF":6.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182767","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}

{"title":"Whispers in the air: Designing acoustic classifiers to detect fruit flies from afar","authors":"Alia Khalid ,&nbsp;Muhammad Latif Anjum ,&nbsp;Salman Naveed ,&nbsp;Wajahat Hussain","doi":"10.1016/j.atech.2024.100738","DOIUrl":"10.1016/j.atech.2024.100738","url":null,"abstract":"<div><div>Detecting weak wingbeats of a flying bug is a challenging problem in uncontrolled outdoor settings. In this work, we show that proper treatment of environmental noise is a key factor in robust acoustic classifier design and propose a novel environmental noise treatment method. Our proposed method generalizes over different classifiers and features. Our algorithm provides robust detection and classification of multiple bugs, over longest ranges reported, using simple microphones. In order to benchmark research in this area, we release a novel dataset containing acoustic data of four bugs (Guava fly, Melon fly, Blue bottle fly, and mosquitoes). We additionally investigate the feasibility of deploying our acoustic classifier on a noisy mobile platform, i.e., a drone. To this end, we expose the limitations of signal processing techniques to deal with loud drone noise. We demonstrate how soundproofing can be used to design acoustic sensing for drones.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100738"},"PeriodicalIF":6.3,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182768","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}

{"title":"Enhancing precision in agriculture: A smart predictive model for optimal sensor selection through IoT integration","authors":"Praveen Sankarasubramanian","doi":"10.1016/j.atech.2024.100749","DOIUrl":"10.1016/j.atech.2024.100749","url":null,"abstract":"<div><div>The rapid advancement in communication technology has sparked a transformative wave across various domains, significantly enhancing comfort and convenience in daily life. Addressing the escalating global demand for food, coupled with the need to alleviate the efforts of farmers, technology, particularly the Internet of Things (IoT), has emerged as a pivotal force. Precisely predicting variations in climatestrictures, ground conditions, and dirt attributes has emerged as a formidable challenge in the realm of agricultural IoT. In this paper, we introduce a smart optimal prediction model for sensors based on IoT-enabled precision agriculture. Initially, we enhance the THAM index (temperature, humidity, air- and water-quality measurement) by using the modified Wild Geese (MWG) algorithm to predict environmental conditions accurately. The deployment of IoT sensor nodes using quantum deep reinforcement learning (QDRL) to determine the idealamount of devices required for effective coverage of the target agricultural field to improving communication. Furthermore, we compute the production yield rate, consider various attributes such as fertilizer regulatory measures, temperature quotient, and agronomy by using the improved prairie dog optimization (IPDO) algorithm. Finally, we assess the performance of MWG-QDRL-IPDO model using test samples collected from the Meteorology Bureau through the related sensor middleware. Our findings reveal a checking efficacy of 96.35 %, even with a reduced amount of devices covering a hugezone. Similarly, the accuracy of IoT sensor node deployment reaches 91.47 %, contributive to reduce the irrelevant data generation and processing time.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100749"},"PeriodicalIF":6.3,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181122","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}

{"title":"Comparative study of citrus fruits (Citrus reticulata Blanco cv. Batu 55) detection and counting with single and double labels based on convolutional neural network using YOLOv7","authors":"Dimas Firmanda Al Riza ,&nbsp;Lucky Candra Musahada ,&nbsp;Romzi Izzudin Aufa ,&nbsp;Mochamad Bagus Hermanto ,&nbsp;Hermawan Nugroho ,&nbsp;Yusuf Hendrawan","doi":"10.1016/j.atech.2024.100763","DOIUrl":"10.1016/j.atech.2024.100763","url":null,"abstract":"<div><div>Fruits detection and counting is an important task for yield prediction that could be achieved by computer vision. The ability to locate and count the fruits could also help the harvesting robot to do a picking task. YOLO is one of the deep learning models which is popular and widely used for object detection and has good performance in detection speed and precision. In the citrus counting task, the label could be set as a single label or multi-label which shows different citrus maturity. The performance of the deep learning model could be different with a different number of labels. Furthermore, there are several types of YOLOv7 models with different sizes and purposes which could also have different performances to do a similar task. This study aims to compare the performance of different kinds of YOLOv7-based deep learning models for citrus fruit detection and counting. Case study on the citrus cv. Batu 55 trees have been carried out. The results show that the original YOLOv7 achieved the best performance both on single and double labels compared to the tiny and X versions of YOLOv7. The YOLOv7 could reach mAP50 of 0.906, a precision of 0.85, a sensitivity of 0.825, and an F1-score of 0.837, while for the counting task, the model has a good performance with R² of 0.966.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100763"},"PeriodicalIF":6.3,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182763","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}

{"title":"Optimizing data collection requirements for machine learning models in wild blueberry automation through the application of DALL-E 2","authors":"Connor C. Mullins,&nbsp;Travis J. Esau,&nbsp;Qamar U. Zaman,&nbsp;Patrick J. Hennessy","doi":"10.1016/j.atech.2024.100764","DOIUrl":"10.1016/j.atech.2024.100764","url":null,"abstract":"<div><div>This research developed a workflow to assess the viability of AI-generated imagery in training machine learning models for detecting ripe wild blueberries (<em>Vaccinium angustifolium</em> Ait.), hair fescue weeds (<em>Festuca filiformis</em> Pourr.), and red leaf disease (<em>Exobasidium vaccinii</em>). Ground truth images were collected and augmented with AI-generated variations using DALL-E 2 to expand the dataset. Models were trained on three datasets: ground truth, generated, and a combination (40% generated images). Evaluation metrics included precision, recall, mAP₅₀, and mAP_50–95, analyzed using ANOVA multiple mean comparisons and Tukey's HSD test (α = 0.05). For ripe wild blueberries, combination models achieved the highest performance across all metrics (mAP₅₀: 0.834), significantly outperforming the ground truth model (mAP₅₀: 0.806) in terms of mAP_50–95 (0.478 compared to 0.424). For hair fescue weeds, the combination dataset outperformed others with the highest mAP₅₀ (0.983), closely followed by the ground truth dataset (mAP₅₀: 0.969). In detecting red leaf disease, the combination dataset showed the best performance (mAP₅₀: 0.848 ± 0.140, mAP_50–95: 0.607 ± 0.219), compared to the ground truth (mAP₅₀: 0.615 ± 0.092, mAP_50–95: 0.417 ± 0.045) and generated datasets (mAP₅₀: 0.245 ± 0.088, mAP_50–95: 0.144 ± 0.059). Models trained solely on generated images showed significantly lower performance across all categories except the precision metric for red leaf, where performance was comparable to ground truth. This indicated that while AI-generated images can augment datasets and improve generalization, they cannot fully replace ground truth data while maintaining model performance. Integrating AI-generated images with real-world data significantly improved model performance, reduced labor-intensive data collection processes, and provided a more diverse and comprehensive dataset for training, underscoring the importance of a balanced approach to optimizing data collection protocols for wild blueberry cultivation.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100764"},"PeriodicalIF":6.3,"publicationDate":"2024-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182766","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}

{"title":"High-accuracy classification of invasive weed seeds with highly similar morphologies: Utilizing hierarchical bilinear pooling for fine-grained image classification","authors":"Lianghai Yang ,&nbsp;Jing Yan ,&nbsp;Xinyue Cao ,&nbsp;Huiru Li ,&nbsp;Binjie Ge ,&nbsp;JiaXin He ,&nbsp;Zhechen Qi ,&nbsp;Xiaoling Yan","doi":"10.1016/j.atech.2024.100758","DOIUrl":"10.1016/j.atech.2024.100758","url":null,"abstract":"<div><div>Invasive weed seeds pose a huge threat to local ecosystems, and it is of great significance to accurately classify invasive weed seeds. Leveraging the rapid advancements in deep learning, various methods have become potential solutions to this problem. In this study, we constructed a large dataset of invasive weed seeds in China and proposed a novel approach to address the identification of species caused by the high similarity among species within the same genus, utilizing Hierarchical Bilinear Pooling (HBP) with ResNet50 as the backbone network. To validate the efficacy of our method, we conducted comparative experiments with classic models in the field of fine-grained recognition. Our evaluation encompassed overall benchmark performance, classification for similar species within the genus, and the classification of species of different sizes. The results demonstrated the HBP-ResNet50 model achieved an outstanding overall benchmark performance accuracy of 99.1 %. Even in <em>Amaranthus</em> and <em>Euphorbia</em> which have highly similar seed morphology, it can achieve high accuracy of 97.94 % and 96.19 %, respectively. The model achieved high accuracy across different sizes of seeds, especially reaching an astonishing 99.18 % in the medium size (1–5 mm). These exceptional results establish the superior performance of HBP-ResNet50. This research has greatly improved the detection efficiency and accuracy, helps curtailing the proliferation of invasive weed seeds, and reduces damage to agricultural ecosystems and economic property losses. The success of our work encourages the future application of this method in the classification of plants, insects, and other relevant fields.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100758"},"PeriodicalIF":6.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143181124","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}

{"title":"An efficientnet-based model for classification of oil palm, coconut and banana trees in drone images","authors":"Kwee Kim Teo ,&nbsp;Nurul Fazmidar Binti Mohd Noor ,&nbsp;Shivakumara Palaiahnakote ,&nbsp;Mohamad Nizam Bin Ayub","doi":"10.1016/j.atech.2024.100748","DOIUrl":"10.1016/j.atech.2024.100748","url":null,"abstract":"<div><div>Oil palm tree detection and classification from coconut and banana trees is vital for increasing the production of oil palm businesses globally, particularly in Malaysia. Since oil palm, coconut, and banana trees share common characteristics such as tree shape and structure, classification is challenging. Further, this work considers images captured by drones, which adds complexity to the classification problem. Unlike most existing methods that primarily detect oil palm trees, the proposed work aims to detect and classify multiple tree types. Inspired by the success of the Segment Anything Model (SAM), a generalized model for object segmentation, we adapted SAM for detecting and localizing oil palm, coconut, and banana trees in drone images. Similarly, motivated by the efficiency and effective feature extraction of EfficientNetB3, we integrated it for the classification task. The proposed model combines SAM for detection and EfficientNetB3 for classification in an end-to-end architecture. To evaluate its performance, we conducted experiments on a dataset collected from a Malaysian drone services company, featuring frames captured across diverse locations. Results demonstrate that the proposed method significantly outperforms state-of-the-art approaches. For detection, the proposed SAM achieves F1-scores of 97 %, 89 %, and 91 % for oil palm, coconut, and banana trees, respectively. For classification, the proposed model achieves F1 scores of 92 %, 88 %, and 91 % for oil palm, coconut, and banana trees, respectively. The results show that the proposed method is superior to the existing methods.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"10 ","pages":"Article 100748"},"PeriodicalIF":6.3,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143182761","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}