Smart agricultural technology最新文献

{"title":"Prediction of the bed expansion and pressure drop in microirrigation media filter backwashing using artificial neural networks and comparison with other machine learning techniques","authors":"Paulino José García-Nieto ,&nbsp;Esperanza García-Gonzalo ,&nbsp;Jonathan Graciano-Uribe ,&nbsp;Gerard Arbat ,&nbsp;Miquel Duran-Ros ,&nbsp;Toni Pujol ,&nbsp;Jaume Puig-Bargués","doi":"10.1016/j.atech.2025.100900","DOIUrl":"10.1016/j.atech.2025.100900","url":null,"abstract":"<div><div>The filtration capacity of media filters, which are widely used in drip irrigation systems to prevent emitter clogging, must be periodically restored by backwashing, which fluidizes the media bed and removes those trapped particles. Bed expansion (BE) and pressure drop (PD) are the key parameters for assessing the hydraulic performance of backwashing, but the available equations and models frequently fall short of their prediction. An experiment with three medium types, four filter underdrain designs, two bed heights and different backwashing superficial velocities as input variables was conducted to measure both BE and PD. A dataset of 705 backwashing runs was obtained and with 80 % of data for training and 20 % for testing, a machine learning-based model that uses Artificial Neural Networks (ANN) to predict both BE and PD was developed and compared with the Ridge, Elastic-net, and Lasso regression models. With coefficients of determination of 0.9932 and 0.9988 for BE and PD, respectively, the results demonstrated that the ANN model not only ranked the importance of the input variables and showed strong agreement with experimental data but also attained superior predictive accuracy regarding the Lasso, Elastic-net, and Ridge models. This study presents a novel and optimized approach for predicting bed expansion and pressure drop, enhancing the reliability of media filter backwashing performance assessments in smart irrigation systems.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100900"},"PeriodicalIF":6.3,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697312","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":"Automated detection and segmentation of baby kale crowns using grounding DINO and SAM for data-scarce agricultural applications","authors":"Gianmarco Goycochea Casas ,&nbsp;Zool Hilmi Ismail ,&nbsp;Mohd Ibrahim Shapiai ,&nbsp;Ettikan Kandasamy Karuppiah","doi":"10.1016/j.atech.2025.100903","DOIUrl":"10.1016/j.atech.2025.100903","url":null,"abstract":"<div><div>This research addresses the significant challenge of data scarcity in agriculture by introducing an automatic pipeline for plant detection and segmentation. The primary objective was to detect and segment the crown area of baby kale (Brassica oleracea var. sabellica) during its early growth stages without relying on extensive data training or manual annotations, providing an alternative for scenarios with insufficient data. A dataset comprising aerial images of baby kale plants was gathered over a three-week period in a controlled environment. The model was processed using the NVIDIA GeForce RTX 4060 GPU. Grounding DINO was employed for plant detection based on textual prompts, and bounding boxes were generated to locate the central plant in each image. The detected regions were then processed using SAM to extract precise segmentation masks of the plant crown. The segmentation results were validated by comparing the automated method with manually annotated ground truth using statistical metrics, including Spearman's correlation, RMSE%, and the Wilcoxon signed-rank test. The automated approach demonstrated a strong correlation (ρ = 0.956) with manual annotations across all weeks, with RMSE% decreasing as plants matured. While Week 1 exhibited lower agreement (ρ = 0.581, RMSE% = 56.246 %) due to segmentation challenges at early growth stages, performance improved significantly in Week 2 (ρ = 0.945, RMSE% = 24.834 %) and Week 3 (ρ = 0.996, RMSE% = 11.733 %). The statistical validation confirmed a significant difference between manual and automated annotations; however, the automated method consistently captured the growth trend of the plants. In conclusion, while the pipeline offers a promising approach for plant detection and segmentation in data-scarce environments, its limitations, especially in early growth stages, should be considered. The study contributes by demonstrating a practical approach to overcoming data scarcity in agriculture using multimodal AI models capable of zero-shot and few-shot learning. This approach paves the way for more adaptive AI-driven agricultural monitoring systems, addressing data scarcity challenges in precision farming.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100903"},"PeriodicalIF":6.3,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685754","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":"Impact of artificial light on photosynthesis, evapotranspiration, and plant growth in plant factories: Mathematical modeling for balancing energy consumption and crop productivity","authors":"Mohammad Hossein Amirshekari,&nbsp;Mohammad Fakhroleslam","doi":"10.1016/j.atech.2025.100901","DOIUrl":"10.1016/j.atech.2025.100901","url":null,"abstract":"<div><div>The impact of artificial light conditions on plants is multifaceted and depends on various influencing factors. Toward optimized energy consumption, understanding the specific requirements of the plant species and tailoring artificial lighting to that, may lead to optimized growth, evapotranspiration (ET), and photosynthetic processes in controlled environments such as indoor farming or plant factories. In this study, an integrated mathematical model has been established to describe relationships between lighting conditions and plants’ growth, ET, and photosynthesis. The developed model also includes the calculation of lamps energy loss, which affects the temperature of the plant factory, and an empirical model for leaf area index (LAI). Additionally, an empirical relationship between plant weight and LAI was developed using experimental data for lettuce plants (<em>Lactuca sativa</em> L.). Key parameters related to photosynthesis and ET for lettuce plants were also accurately adjusted, and the validation results were discussed. Based on the developed model, the effects of light intensity and photoperiod on photosynthesis, LAI, plant weight, and ET were analyzed. Results demonstrate that the effect of the photoperiod on photosynthesis and ET is significantly greater than its effect on plant weight and LAI. However, the impact of light intensity on photosynthesis, ET, plant weight, and LAI is approximately the same. The proposed integrated model can be used to simulate microclimate conditions, optimize resource use, and improve the control of plant factories.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100901"},"PeriodicalIF":6.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685755","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":"A state-of-the-art novel approach to predict potato crop coefficient (Kc) by integrating advanced machine learning tools","authors":"Saad Javed Cheema ,&nbsp;Masoud Karbasi ,&nbsp;Gurjit S. Randhawa ,&nbsp;Suqi Liu ,&nbsp;Travis J. Esau ,&nbsp;Kuljeet Singh Grewal ,&nbsp;Farhat Abbas ,&nbsp;Qamar Uz Zaman ,&nbsp;Aitazaz A. Farooque","doi":"10.1016/j.atech.2025.100896","DOIUrl":"10.1016/j.atech.2025.100896","url":null,"abstract":"<div><div>The crop coefficient (<em>K_c</em>) is one of the important elements of the actual evapotranspiration estimation. The current study aims to develop a machine learning approach to estimate the crop coefficient of potatoes (Russet Burbank variety) in Prince Edward Island province, one of Canada's most important producers. The study was conducted at drainage-type lysimeters placed in the potato field with three types of soils (sandy loam, loamy sand, and loam). A machine learning approach using XGBoost, optimized with the Chaos Game algorithm (CGO-XGBoost), was employed to predict <em>K_c</em>. Three input scenarios (meteorological + soil data, soil-only, meteorological-only) were tested. Three other machine learning techniques, K-nearest neighbor (KNN), Adaptive Boosting (AdaBoost), and Multilayer Perceptron Neural Network (MLP), were used to compare with the newly developed model. Different performance metrics such as correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to compare different model's performance. Results showed that the CGO-XGBoost model outperformed conventional machine learning models. A comparison of different input scenarios revealed that combination 2 (Soil data only) gave the best results. Combination 3 (only meteorological data) performs weakest among input scenarios. The best model (Combination2 + CGO-XGBoost) achieved the highest accuracy (R = 0.9961, RMSE = 0.0185, MAPE = 2.20%), outperforming traditional methods. SHapley Additive exPlanations (SHAP) interpretability analysis indicates that soil moisture exerts the greatest impact on potato <em>K_c</em>. Field Capacity (FC) and Minimum temperature rank as the second and third most significant factors. The integration of SHAP values in the proposed solution improves the interpretability of the model, offering valuable insights into the environmental and soil factors affecting <em>K_c</em> predictions. The results showed that the proposed model can accurately predict <em>K_c</em>, demonstrating its potential to enhance water-use efficiency and support precision irrigation strategies.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100896"},"PeriodicalIF":6.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714668","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":"Development of a color-based, non-destructive method to determine leaf N levels of Hass avocado under field conditions","authors":"Ángeles Gallegos ,&nbsp;Mayra E. Gavito ,&nbsp;Heberto Ferreira-Medina ,&nbsp;Eloy Pat ,&nbsp;Marta Astier ,&nbsp;Sergio Rogelio Tinoco-Martínez ,&nbsp;Yair Merlín-Uribe ,&nbsp;Carlos E. González-Esquivel","doi":"10.1016/j.atech.2025.100895","DOIUrl":"10.1016/j.atech.2025.100895","url":null,"abstract":"<div><div>Excessive fertilization in avocado trees might be avoided by providing producers with affordable supporting tools for constant monitoring of nutrient levels. Leaf color guides have been produced for cereals and might be useful, but they are so far rare for trees because of low variation in color. We investigated the potential of leaf color to indicate N and P levels in avocado leaves to develop a monitoring tool not requiring expensive chemical analyses. We carried out three experimental phases towards the development of a solid, reproducible monitoring tool. In the first phase, we found a good relation between color and chemically-measured N levels, but not P levels. That allowed us to develop a leaf color chart only for N levels. In the second phase, this visual guide was tested using print and mobile app versions. We found that visual identification of N levels by the users was highly variable, subjective, and prone to error regardless of the materials used for detection. The third phase aimed to develop a user-independent evaluation of leaf color to define the leaf N level using leaf pictures. Machine and deep learning algorithms were used to generate, calibrate, and validate models for estimating the N concentration of avocado leaves using digital images captured in field conditions. Applying the models generated, we can now develop an automated color detection and N-level identification tool for mobile applications that will assist avocado producers in adequate application of nitrogen fertilizers, saving money and reducing N pollution from leaching in orchards.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100895"},"PeriodicalIF":6.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697316","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 resilience in specialty crop production in a changing climate through smart systems adoption","authors":"Patience Chizoba Mba,&nbsp;Judith Nkechinyere Njoku,&nbsp;Daniel Dooyum Uyeh","doi":"10.1016/j.atech.2025.100897","DOIUrl":"10.1016/j.atech.2025.100897","url":null,"abstract":"<div><div>Climate change critically impacts agriculture, particularly specialty crop production. This paper examines the effects on high-value fruits, nuts, and herbs, emphasizing challenges in rural and developing areas. Specialty crops are susceptible to climatic variations, affecting their yield, quality, and economic viability. Changing temperature and precipitation patterns and increased pest and disease prevalence pose significant threats, leading to potential food security and economic stability issues. Integrating smart systems, such as precision agriculture and sensor technologies, offers viable solutions to mitigate these impacts. These systems enable real-time monitoring and adjustment of environmental conditions, optimizing resource usage and enhancing crop management practices. This paper highlights the importance of building resilience through innovative farming techniques, sustainable practices, and robust research. Adopting these strategies helps farmers protecting their crops against the adverse effects of climate change, ensuring long-term productivity and economic stability.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100897"},"PeriodicalIF":6.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747501","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 automated carcass segmentation and composition quantification in live pigs via large-scale CT imaging and its application in genetic analysis of pig breeding","authors":"Haoqi Xu ,&nbsp;Zhenyang Zhang ,&nbsp;Wei Zhao ,&nbsp;Yizheng Zhuang ,&nbsp;Xiaoliang Hou ,&nbsp;Yongqi He ,&nbsp;Jianlan Wang ,&nbsp;Jiongtang Bai ,&nbsp;Yan Fu ,&nbsp;Zhen Wang ,&nbsp;Yuchun Pan ,&nbsp;Qishan Wang ,&nbsp;Zhe Zhang","doi":"10.1016/j.atech.2025.100898","DOIUrl":"10.1016/j.atech.2025.100898","url":null,"abstract":"<div><div>Carcass segmentation and composition (CSC) traits are important indicators for assessing the economic efficiency of pig production. Conventional determination of these traits by slaughter has the drawbacks of high costs and the inability to retain breeding stock. Combining computed tomography (CT) with deep learning enables the non-invasive evaluation of live animal carcass characteristics. In this study, we proposed UPPECT for predicting CSC traits of live pigs based on deep learning. A labeled dataset comprising 300 pigs with a total of 63,708 CT images was constructed for training the nnU-Net model to automatically segment different cuts of pig carcasses. The composition quantification process was optimized using adaptive thresholding and bone filling to achieve accurate prediction of 16 CSC traits. At last, the genetic parameters of CSC traits obtained by UPPECT were estimated for 4,063 pigs. The segmentation model demonstrated excellent performance with a PA of 0.9992, an IoU of 0.9910 and an F1-score of 0.9955. We slaughtered and dissected 50 pigs to obtain real CSC trait values as the validation dataset. The results showed that our method improved the accuracy of composition quantification after optimization, and our predictions for all traits were highly correlated with manual dissection results, with correlation coefficients up to 0.9568. The heritability estimates ranged from 0.52 to 0.85 for all traits. Our study enables non-invasive and precise measurement of CSC traits of live pigs, which makes an important contribution to the breeding practice. A graphical user interface software for UPPECT is freely accessible at <span><span>https://github.com/StMerce/UPPECT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100898"},"PeriodicalIF":6.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685753","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":"Optical leaf area assessment supports chlorophyll estimation from UAV images","authors":"Klára Pokovai ,&nbsp;János Mészáros ,&nbsp;Kitti Balog ,&nbsp;Sándor Koós ,&nbsp;Mátyás Árvai ,&nbsp;Nándor Fodor","doi":"10.1016/j.atech.2025.100894","DOIUrl":"10.1016/j.atech.2025.100894","url":null,"abstract":"<div><div>Measurement of crop chlorophyll content provides information on expected yield at an early stage of vegetation development. Spectral vegetation indices (VIs) are closely related with crop chlorophyll content and nowadays they became common tools for estimating parameters of vegetation monitoring in field scale. Thus, the objectives of this study were to validate the correlation of VIs (calculated from drone-based hyperspectral images) with leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) of crops grown at three different nitrogen levels at two experimental sites. LCC and leaf area index (LAI) were measured with handheld devices. The effect of vegetation size, expressed as two LAI ranges of canopy, on the magnitude of the resulting correlations was investigated as well. Our results showed that for less developed vegetation (LAI &lt; 2.7), all studied VIs are suitable for assessing chlorophyll content. However, at higher LAI values, some VIs had no significant correlation with either LCC or CCC. Based on linear regression, NDRE for less developed vegetation (LAI &lt; 2.7), as well as NDRE, CI_RE or SR_RE for closed vegetation (LAI &gt; 2.7), are recommended for monitoring chlorophyll content when the LAI of the vegetation is known and therefore the CCC can be derived. We conclude that drone imagery may greatly assists farmers in observing biophysical characteristics, but is limited for observing chlorophyll status within crops of closed vegetation size.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100894"},"PeriodicalIF":6.3,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143685751","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":"Artificial intelligence applied to precision livestock farming: A tertiary study","authors":"Damiano Distante ,&nbsp;Chiara Albanello ,&nbsp;Hira Zaffar ,&nbsp;Stefano Faralli ,&nbsp;Domenico Amalfitano","doi":"10.1016/j.atech.2025.100889","DOIUrl":"10.1016/j.atech.2025.100889","url":null,"abstract":"<div><div>Recent advances in Artificial Intelligence (AI) are transforming the livestock sector by enabling continuous real-time data monitoring and automated decision support systems. While several secondary studies have explored the application of AI in Precision Livestock Farming (PLF), they often focus on specific AI techniques or particular PLF activities, limiting a broader understanding of the field. This study aims to provide a comprehensive overview of the state-of-the-art of AI applications in PLF, highlighting both achievements and areas that require further investigation. To this end, a tertiary systematic mapping study was conducted following recognized guidelines to ensure reliability and replicability. The research process involved formulating 10 research questions, designing a comprehensive search strategy, and performing a rigorous quality assessment of the identified studies. From an initial pool of 738 retrieved manuscripts, 14 high-quality secondary studies were selected and analyzed. The findings reveal a wide range of AI techniques applied in PLF, particularly in the learning and perception AI domains. These techniques have proven effective in tasks such as animal recognition, abnormality detection, and health and welfare monitoring. However, comparatively less attention has been given to environmental monitoring and sustainability, highlighting an area that warrants further exploration. By offering valuable insights for future research and practical applications, this study suggests directions for both researchers and livestock farmers to unlock AI's full potential in PLF.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100889"},"PeriodicalIF":6.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637568","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":"Optimization of irrigation and fertigation in smart agriculture: An IoT-based micro-services framework","authors":"Tommaso Adamo ,&nbsp;Danilo Caivano ,&nbsp;Lucio Colizzi ,&nbsp;Giovanni Dimauro ,&nbsp;Emanuela Guerriero","doi":"10.1016/j.atech.2025.100885","DOIUrl":"10.1016/j.atech.2025.100885","url":null,"abstract":"<div><div>Efficient management of water and fertilizer resources is crucial for achieving sustainability and productivity in agriculture. This paper presents an AI-powered microservices solution that optimizes irrigation and fertigation practices. The proposed system integrates IoT nodes for real-time data collection on environmental conditions, soil moisture levels, and nutrient crop needs. Fertigation and irrigation decision-making are modeled as a data-driven sequential decision problem. At each decision stage, real-time data serve as input to an AI planning model aimed at satisfying nutrient and water demands while minimizing water and fertilizer waste. The system allows supervision by the farmer through a mobile app and a Digital Twin, enabling the design of crop planting layouts and providing detailed information on real-time decisions implemented in the field, as well as water and fertilizer consumption. The proposed solution manages diverse crop species with distinct water and nutrient requirements. Efficient data exchange is facilitated through a push-pull communication paradigm between the IoT nodes and cloud services. This approach offers several benefits, including greater control over data flow, energy savings, and increased flexibility in resource management.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100885"},"PeriodicalIF":6.3,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620831","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}