Artificial Intelligence in Agriculture最新文献

{"title":"Improving the non-destructive maturity classification model for durian fruit using near-infrared spectroscopy","authors":"Sirirak Ditcharoen ,&nbsp;Panmanas Sirisomboon ,&nbsp;Khwantri Saengprachatanarug ,&nbsp;Arthit Phuphaphud ,&nbsp;Ronnarit Rittiron ,&nbsp;Anupun Terdwongworakul ,&nbsp;Chayuttapong Malai ,&nbsp;Chirawan Saenphon ,&nbsp;Lalita Panduangnate ,&nbsp;Jetsada Posom","doi":"10.1016/j.aiia.2023.02.002","DOIUrl":"https://doi.org/10.1016/j.aiia.2023.02.002","url":null,"abstract":"<div><p>The maturity state of durian fruit is a key indicator of quality before trading. This research aims to improve the near-infrared (NIR) model for classifying the maturity stage of durian fruit using a completely non-destructive measurement. Both NIR spectrometers were investigated: the short wavelength NIR (SWNIR) ranging from 450 to 1000 nm and long wavelength NIR (LWNIR) ranging from 860 to 1750 nm. The samples collected for experimentation consisted of four stages: immaturity, prematurity, maturity, and ripe. Each fruit was scanned at the rind position on the main fertile lobe (header, middle, and tail) and stem. The classification models were developed using three supervised machine learning algorithms: linear discriminant analysis (LDA), support vector machine (SVM), and K-Nearest neighbours (KNN). The analysis results revealed that the use of durian rind spectra only obtained between 83.15% and 88.04% accuracy for the LWNIR spectrometer, while the SWNIR spectrometer provided 64.73 to 93.77% accuracy. The performance of model increases when developing with combination between rind and stem spectra. The LDA model developed using a combination of rind and stem spectra provided the greatest efficiency, exhibiting 97.28% and 100% accuracy for LWNIR and SWNIR spectrometers, respectively. The LDA model is therefore recommended for obtaining spectra from smoothing moving average (MA) + baseline of rind position and when used in combination with the MA + standard normal variance (SNV) of stem spectra. The NIR spectroscopy indicated high potential for non-destructive estimation of the durian maturity stage. This process could be used for quality control in the durian export industry to solve the problem of unripe durian being mixed with ripe fruit.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"7 ","pages":"Pages 35-43"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50189221","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 fuzzy risk assessment model used for assessing the introduction of African swine fever into Australia from overseas","authors":"Hongkun Liu ,&nbsp;YongLin Ren ,&nbsp;Huanhuan Chu ,&nbsp;Hu Shan ,&nbsp;Kok Wai Wong","doi":"10.1016/j.aiia.2023.02.001","DOIUrl":"10.1016/j.aiia.2023.02.001","url":null,"abstract":"<div><p>African swine fever (ASF) is a contagious and lethal hemorrhagic disease with a high case fatality rate. Since 2007, ASF has been spreading into many countries, especially in Europe and Asia. Given that there is no effective vaccine and treatment to deal with ASF, prevention is an important way for a country to avoid the effects of the virus. Australia is currently ASF-free but the disease has been reported in many neighboring countries, such as Indonesia, Timor-Leste, and Papua New Guinea. Therefore, it is necessary for Australia to maintain hyper-vigilance to prevent the ASF introduction. In this paper, we propose the use of fuzzy concepts to establish a fuzzy risk assessment model to predict the ASF introduction risk in Australia. From the analysis, the international passengers (IP) and international import trade (IIT) are concluded as the two main ASF introduction factors based on transmission features and past research. From the established fuzzy risk assessment model based on the analysis of the 2019 and 2020 data, the risks of ASF introduction into Australia are considered to be low. The model further deduced that the Asian region was the major source of potential risks. Finally, in order to validate the effectiveness of the established fuzzy risk assessment model, the qualitative data from the Department for Environment, Food &amp; Rural Affairs of the United Kingdom was used. From the validation results, it has shown that the results were consistent when the same data is adopted, and thus proved that the functionality of the established fuzzy risk assessment model for assessing the risk in Australia.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"7 ","pages":"Pages 27-34"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43993437","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":"t-SNE: A study on reducing the dimensionality of hyperspectral data for the regression problem of estimating oenological parameters","authors":"Rui Silva ,&nbsp;Pedro Melo-Pinto","doi":"10.1016/j.aiia.2023.02.003","DOIUrl":"10.1016/j.aiia.2023.02.003","url":null,"abstract":"<div><p>In recent years there is a growing importance in using machine learning techniques to improve procedures in precision agriculture: in this work we perform a study on models capable of predicting oenological parameters from hyperspectral images of wine grape berries, a specially relevant topic to boost production tasks for winemakers. Specifically, we explore the capabilities of a novel technique mostly used for visualization, t-Distributed Stochastic Neighbor Embedding (t-SNE), for reducing the dimensionality of the highly complex hyperspectral data and compare its performance with Principal Component Analysis (PCA) method, which despite the introduction of many nonlinear dimensionality reduction techniques over the years, had achieved the best results for real-world data across several studies in literature. Additionally we explore the potential of Kernel t-SNE, an extension to the t-SNE method that allows for the usage of the technique in streaming data or online scenarios. Our results show that, in a direct comparison, t-SNE achieves better metrics than PCA for most of the data sets in this work and that the regressor (Support Vector Regression, SVR) performs better with the t-SNE reduced features as inputs, accomplishing better predictions with lower error rates. Comparing the results with current literature, our shallow learning model paired with t-SNE achieves either better or on par results than those reported, even competing with more advanced models that use deep learning techniques, which should propel the introduction of t-SNE in more studies that require dimensionality reduction.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"7 ","pages":"Pages 58-68"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41572754","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":"Deep learning models for automatic identification of plant-parasitic nematode","authors":"Nabila Husna Shabrina ,&nbsp;Ryukin Aranta Lika ,&nbsp;Siwi Indarti","doi":"10.1016/j.aiia.2022.12.002","DOIUrl":"10.1016/j.aiia.2022.12.002","url":null,"abstract":"<div><p>Plant-parasitic nematodes cause various diseases that can be fatal to the infected plants. It causes losses to the agricultural industry, such as crop failure and poor crop quality. Developing an accurate nematode classification system is vital for pest identification and control. Deep learning classification techniques can help speed up Nematode identification as it can perform tasks directly from images. In the present study, four state-of-the-art deep learning models (ResNet101v2, CoAtNet-0, Effi- cientNetV2B0, and EfficientNetV2M) were evaluated in plant-parasitic nematode classification from microscopic image. The models were trained using a combination of three different optimizers (Adam, SGD, dan RMSProp) and several data augmentation with image transformations, such as image flip, blurring, noise addition, brightness, and contrast adjustment. The performance of the trained models was varied. Regarding test accuracy, EfficientNetV2B0 and EfficientNetV2M using RMSProp and brightness augmentation give the best result of 97.94% However, the overall performance of EfficientNetV2M was superior, with 98.66% mean class accuracy, 97.99%F1 score, 98.26% average precision, and 97.94% average recall.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"7 ","pages":"Pages 1-12"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45799752","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":"Review of agricultural IoT technology","authors":"Jinyuan Xu ,&nbsp;Baoxing Gu ,&nbsp;Guangzhao Tian","doi":"10.1016/j.aiia.2022.01.001","DOIUrl":"10.1016/j.aiia.2022.01.001","url":null,"abstract":"<div><p>Agricultural Internet of Things (IoT) has brought new changes to agricultural production. It not only increases agricultural output but can also effectively improve the quality of agricultural products, reduce labor costs, increase farmers' income, and truly realize agricultural modernization and intelligence. This paper systematically summarizes the research status of agricultural IoT. Firstly, the current situation of agricultural IoT is illustrated and its system architecture is summarized. Then, the five key technologies of agricultural IoT are discussed in detail. Next, applications of agricultural IoT in five representative fields are introduced. Finally, the problems existing in agricultural IoT are analyzed and a forecast is given of the future development of agricultural IoT.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 10-22"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000010/pdfft?md5=275e83aefc903613e884982969de9e88&pid=1-s2.0-S2589721722000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46314466","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":"Effect and economic benefit of precision seeding and laser land leveling for winter wheat in the middle of China","authors":"Jing Chen ,&nbsp;Chunjiang Zhao ,&nbsp;Glyn Jones ,&nbsp;Hao Yang ,&nbsp;Zhenhong Li ,&nbsp;Guijun Yang ,&nbsp;Liping Chen ,&nbsp;Yongchang Wu","doi":"10.1016/j.aiia.2021.11.003","DOIUrl":"10.1016/j.aiia.2021.11.003","url":null,"abstract":"<div><p>Rapid socio-economic changes in China, such as land conversion and urbanization, are creating new scopes for the application of precision agriculture (PA).An experiment to assess the economic benefits of two precision agriculture methods was applied for one year – precision seeding and precision seeding with land leveling. Whilst the results for this were positive, of itself it did not provide evidence of longer terms gains. The costs of land leveling are accrued in a single year but the benefits could carry over into subsequent years. Thus, in this case if the PA method provides carry over benefits to future years, the economic assessment would incorrectly assign all the costs to a single year of benefits i.e.the benefit-cost ratio would be underestimated. To gauge whether there was carry over benefits in future years we looked at NDVI and GUI as proxies for future year benefits. For the single year experiment, our results showed that: (1) Winter wheat yield was increased 23.2% through the integration of precision seeding and laser leveling technologies.(2) Both the single technology and the integrated technologies significant reduced the concentration of soil ammonium nitrogen at the depths of 60 cm; (3) The benefit/cost ratio's of the treatments exceeded that of the baseline by approximately 10% which translated to an increase of several hundred US$ per hectare. The NDVI analysis showed that the effect of laser land leveling could last to the next two years. When considering the multi-year impact of land leveling, the benefit/cost ratio of PSLL will increase to 23.5% and 22.9% with and without laser land leveling subsidies. Making clear the economic benefits of using PA technologies will likely promote application of the technologies in the region.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 1-9"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721721000349/pdfft?md5=0c5c4f99c9af286d04dc0a8a2a5766e3&pid=1-s2.0-S2589721721000349-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46124768","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":"Nutrient optimization for plant growth in Aquaponic irrigation using Machine Learning for small training datasets","authors":"Sambandh Bhusan Dhal ,&nbsp;Muthukumar Bagavathiannan ,&nbsp;Ulisses Braga-Neto ,&nbsp;Stavros Kalafatis","doi":"10.1016/j.aiia.2022.05.001","DOIUrl":"https://doi.org/10.1016/j.aiia.2022.05.001","url":null,"abstract":"<div><p>With the recent trends in urban agriculture and climate change, there is an emerging need for alternative plant culture techniques where dependence on soil can be eliminated. Hydroponic and aquaponic growth techniques have proven to be viable alternatives, but the lack of efficient and optimal practices for irrigation and nutrient supply limits its applications on a large-scale commercial basis. The main purpose of this research was to develop statistical methods and Machine Learning algorithms to regulate nutrient concentrations in aquaponic irrigation water based on plant needs, for achieving optimal plant growth and promoting broader adoption of aquaponic culture on a commercial scale. One of the key challenges to developing these algorithms is the sparsity of data which requires the use of Bolstered error estimation approaches. In this paper, several linear and non-linear algorithms trained on relatively small datasets using Bolstered error estimation techniques were evaluated, for selecting the best method in making decisions regarding the regulation of nutrients in hydroponic environments. After repeated tests on the dataset, it was decided that Semi-Bolstered Resubstitution Error estimation technique works best in our case using Linear Support Vector Machine as the classifier with the value of penalty parameter set to one. A set of recommended rules have been prescribed as a Decision Support System, using the output of the Machine Learning algorithm, which have been tested against the results of the baseline model. Further, the positive impact of the recommended nutrient concentrationson plant growth in aquaponic environments has been elaborately discussed.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 68-76"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000058/pdfft?md5=88b4d1d7657cf1e51d82120cbd2cc4e8&pid=1-s2.0-S2589721722000058-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91954151","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 systematic review of machine learning techniques for cattle identification: Datasets, methods and future directions","authors":"Md Ekramul Hossain ,&nbsp;Muhammad Ashad Kabir ,&nbsp;Lihong Zheng ,&nbsp;Dave L. Swain ,&nbsp;Shawn McGrath ,&nbsp;Jonathan Medway","doi":"10.1016/j.aiia.2022.09.002","DOIUrl":"10.1016/j.aiia.2022.09.002","url":null,"abstract":"<div><p>Increased biosecurity and food safety requirements may increase demand for efficient traceability and identification systems of livestock in the supply chain. The advanced technologies of machine learning and computer vision have been applied in precision livestock management, including critical disease detection, vaccination, production management, tracking, and health monitoring. This paper offers a systematic literature review (SLR) of vision-based cattle identification. More specifically, this SLR is to identify and analyse the research related to cattle identification using Machine Learning (ML) and Deep Learning (DL). This study retrieved 731 studies from four online scholarly databases. Fifty-five articles were subsequently selected and investigated in depth. For the two main applications of cattle detection and cattle identification, all the ML based papers only solve cattle identification problems. However, both detection and identification problems were studied in the DL based papers. Based on our survey report, the most used ML models for cattle identification were support vector machine (SVM), k-nearest neighbour (KNN), and artificial neural network (ANN). Convolutional neural network (CNN), residual network (ResNet), Inception, You Only Look Once (YOLO), and Faster R-CNN were popular DL models in the selected papers. Among these papers, the most distinguishing features were the muzzle prints and coat patterns of cattle. Local binary pattern (LBP), speeded up robust features (SURF), scale-invariant feature transform (SIFT), and Inception or CNN were identified as the most used feature extraction methods. This paper details important factors to consider when choosing a technique or method. We also identified major challenges in cattle identification. There are few publicly available datasets, and the quality of those datasets are affected by the wild environment and movement while collecting data. The processing time is a critical factor for a real-time cattle identification system. Finally, a recommendation is given that more publicly available benchmark datasets will improve research progress in the future.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 138-155"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000125/pdfft?md5=5c303aaed4d0b316e8d46d6fdcabbce5&pid=1-s2.0-S2589721722000125-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73539392","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":"Land suitability analysis for maize production using geospatial technologies in the Didessa watershed, Ethiopia","authors":"Mitiku Badasa Moisa ,&nbsp;Firdissa Sadeta Tiye ,&nbsp;Indale Niguse Dejene ,&nbsp;Dessalegn Obsi Gemeda","doi":"10.1016/j.aiia.2022.02.001","DOIUrl":"10.1016/j.aiia.2022.02.001","url":null,"abstract":"<div><p>Physical land suitability assessment is a prerequisite for enhancing yield production and enables the agricultural communities to use the right place for the right crops. Maize is one of stable one food crops of Ethiopia and cultivated in three agroecological zones: highland, midland and lowlands. Despite these facts, maize yield is very low due to a lack of knowledge and information gaps on land suitability. Physical land suitability for maize cultivation is essential to minimize the problem of food security. The present study aims to identify the potential land suitability for maize production in the Didessa watershed, Western Ethiopia using Multi-Criteria Evaluation (MCE) and geospatial technologies. Land use land cover (LULC) change, climate, topography, soil, and infrastructure facilities were considered for maize land suitability assessment. The MCE based pairwise comparison matrix was applied to estimate land suitability for maize crop cultivation. The results showed that, about 977.7 km² (14.1%) is highly suitable, 4794.9 km²(69.1%) is moderately suitable while 1118.8 km² (16.1%), and 51.5 km² (0.7%) of the study area were categorized under marginally and not suitable for maize production, respectively. This research provides crucial information for decision making organs and the farming community to utilize potential areas for maize cultivation.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 34-46"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000022/pdfft?md5=37f7314e4592335ab85dfba8861ac549&pid=1-s2.0-S2589721722000022-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46804279","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":"Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG","authors":"Ananda S. Paymode,&nbsp;Vandana B. Malode","doi":"10.1016/j.aiia.2021.12.002","DOIUrl":"10.1016/j.aiia.2021.12.002","url":null,"abstract":"<div><p>In recent times, the use of artificial intelligence (AI) in agriculture has become the most important. The technology adoption in agriculture if creatively approached. Controlling on the diseased leaves during the growing stages of crops is a crucial step. The disease detection, classification, and analysis of diseased leaves at an early stage, as well as possible solutions, are always helpful in agricultural progress. The disease detection and classification of different crops, especially tomatoes and grapes, is a major emphasis of our proposed research. The important objective is to forecast the sort of illness that would affect grapes and tomato leaves at an early stage. The Convolutional Neural Network (CNN) methods are used for detecting Multi-Crops Leaf Disease (MCLD). The features extraction of images using a deep learning-based model classified the sick and healthy leaves. The CNN based Visual Geometry Group (VGG) model is used for improved performance measures. The crops leaves images dataset is considered for training and testing the model. The performance measure parameters, i.e., accuracy, sensitivity, specificity precision, recall and F1-score were calculated and monitored. The main objective of research with the proposed model is to make on-going improvements in the performance. The designed model classifies disease-affected leaves with greater accuracy. In the experiment proposed research has achieved an accuracy of 98.40% of grapes and 95.71% of tomatoes. The proposed research directly supports increasing food production in agriculture.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 23-33"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721721000416/pdfft?md5=6efb65071e9550352409895eda1a2383&pid=1-s2.0-S2589721721000416-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47509400","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}