Information Processing in Agriculture最新文献

{"title":"Innovative deep learning approach for cross-crop plant disease detection: A generalized method for identifying unhealthy leaves","authors":"Imane Bouacida ,&nbsp;Brahim Farou ,&nbsp;Lynda Djakhdjakha ,&nbsp;Hamid Seridi ,&nbsp;Muhammet Kurulay","doi":"10.1016/j.inpa.2024.03.002","DOIUrl":"10.1016/j.inpa.2024.03.002","url":null,"abstract":"<div><div>One of the most serious threats to global food security is plant diseases compromising agricultural productivity and threatening the livelihoods of millions. These diseases can decimate crops, disrupt food supply chains, and escalate the risk of food shortages, underscoring the urgency of implementing robust strategies to safeguard the world’s food sources. Deep learning methods have revolutionized the field of plant disease detection, offering advanced and accurate solutions for early identification and management. However, a recurring problem in deep learning models is their susceptibility to a lack of robustness and generalization when facing novel crop and disease types that were not included in the training dataset. In this paper, we address this issue by proposing a novel deep learning-based system capable of recognizing diseased and healthy leaves across different crops, even if the system was not trained on them. The key idea is to focus on recognizing the diseased small leaf regions rather than the overall appearance of the diseased leaf, along with determining the disease’s prevalence rate on the entire leaf. For efficient classification and to leverage the excellence of the Inception model in disease recognition, we employ a small Inception model architecture, which is suitable for processing small regions without compromising performance. To confirm the effectiveness of our method, we trained and tested it using the widely acclaimed PlantVillage dataset, recognized as the most utilized dataset for its comprehensive and diverse coverage. Our method achieved an accuracy rate of 94.04%. Furthermore, when tested on new datasets, it achieved an accuracy rate of 97.13%. This innovative approach not only enhances the accuracy of plant disease detection but also addresses the critical challenge of model generalization to diverse crops and diseases. In addition, it outperformed the existing methods in its ability to identify any disease across any crop type, showcasing its potential for broad applicability and contribution to global food security initiatives.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 54-67"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140085028","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 severity estimation method for lightweight cucumber leaf disease based on DM-BiSeNet","authors":"Kaiyu Li ,&nbsp;Yuzhaobi Song ,&nbsp;Xinyi Zhu ,&nbsp;Lingxian Zhang","doi":"10.1016/j.inpa.2024.03.003","DOIUrl":"10.1016/j.inpa.2024.03.003","url":null,"abstract":"<div><div>Accurately estimating the severity of cucumber diseases is crucial for improving cucumber quality and minimizing economic losses. Deep learning techniques have shown promising results in automatically extracting disease image features for severity estimation. However, existing methods still face challenges in accurately estimating disease severity under complex backgrounds and achieving real-time performance.This paper presents a lightweight severity estimation method called DM-BiSeNet to address these challenges. The proposed method utilizes BiSeNet V2 as the base network and incorporates depthwise separable convolutional blocks to optimize the detail branch. A simplified MobileNet V3 network is also constructed to optimize the semantic branch. The model training process is accelerated using the AdamW optimizer. To evaluate the performance of DM-BiSeNet, a dataset consisting of cucumber powdery mildew and downy mildew disease images collected in natural scenes is utilized. Experimental results demonstrate that DM-BiSeNet achieves higher accuracy in severity estimation, with an <em>R²</em> value of 0.9407 and an RMSE of 1.0680, outperforming the comparison methods. Moreover, DM-BiSeNet exhibits a complexity of 1.54 GFLOPs and is capable of reasoning 94 disease images per second.The proposed DM-BiSeNet model offers a lightweight and effective solution for accurate and rapid severity estimation of cucumber diseases under complex backgrounds. It provides a valuable technical tool for quantitative disease estimation, offering significant potential for practical applications.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 68-79"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534658","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":"Improving crop image recognition performance using pseudolabels","authors":"Pengfei Deng,&nbsp;Zhaohui Jiang,&nbsp;Huimin Ma,&nbsp;Yuan Rao,&nbsp;Wu Zhang","doi":"10.1016/j.inpa.2024.02.001","DOIUrl":"10.1016/j.inpa.2024.02.001","url":null,"abstract":"<div><div>In crop image recognition, when faced with a large quantity of unlabeled data, the traditional manual labeling method requires a large amount of human and material resources. To solve this problem, this study proposes an image recognition method based on a pseudolabeling technique. First, the data are divided into labeled and unlabeled data. The initial network model is trained on labeled data. Then, pseudolabeling of the unlabeled data is predicted, and only the data that satisfy the confidence threshold are regarded as valid pseudolabeling. To convert the unlabeled data into supervised training data, the two types of data are mixed. The training is terminated when the number of remaining unlabeled data satisfies the end condition and when the fivefold cross-validation method is used to evaluate model performance. Compared with the traditional semisupervised method, the experimental method is simpler and more applicable. Experiments were conducted on rice growth stage recognition and crop weed seedling recognition tasks. The results showed that the proposed method achieved 99.17% accuracy in rice growth stage recognition and a high AUC value of 99.93% in crop weed seedling recognition, which demonstrated excellent performance. Compared with the traditional model, this method not only improves in accuracy but also has better stability and wider applicability and is expected to provide an efficient, accurate and scalable solution for crop image recognition.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 17-26"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534657","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":"Security analysis of agricultural energy internet considering electricity load control for dragon fruit cultivation","authors":"Xueqian Fu ,&nbsp;Lingxi Ma ,&nbsp;Huaichang Ge ,&nbsp;Jiahui Zhang","doi":"10.1016/j.inpa.2024.02.002","DOIUrl":"10.1016/j.inpa.2024.02.002","url":null,"abstract":"<div><div>With the increasing emphasis on sustainable energy and the advancements in modern agriculture, flexible agricultural power loads present challenges to the reliable operation of the agricultural energy internet. However, research on the coupling of energy system security with agricultural security is insufficient and fails to consider the impacts of meteorological elements on agricultural power loads. To address these gaps, this paper establishes load models for irrigation and light supplementation based on the actual cultivation demands of winter dragon fruit in Guangxi province. A static security index system is developed to analyze the security, considering the unique features of agricultural power demands. The condition of the distribution network is assessed by comparing the indexes with predefined limits, using a China 41-bus distribution network. Finally, the optimal scheme for nocturnal supplemental lighting treatment and irrigation is determined based on the method for maintaining secure operation of the distribution network. This study serves as a guide for simulating current farming power loads and demonstrates how security analysis of the agricultural energy internet contributes to the large-scale and sophisticated development of modern agriculture.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 1","pages":"Pages 27-39"},"PeriodicalIF":7.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140466733","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":"Evaluation of drying behavior and characteristics of potato slices in multi–stage convective cabinet dryer: Application of artificial neural network","authors":"Susama Chokphoemphun ,&nbsp;Somporn Hongkong ,&nbsp;Suriya Chokphoemphun","doi":"10.1016/j.inpa.2023.06.003","DOIUrl":"10.1016/j.inpa.2023.06.003","url":null,"abstract":"<div><div>The inconsistency in the quality of dried products at different coordinates within a conventional multi-stage convective cabinet dryer is a critical but often neglected problem. In this study, the drying behavior (moisture ratio) occurring in each drying tray layer and the drying characteristics (shrinkage or area ratio) occurring at different coordinates within a multi-stage convective cabinet dryer was assessed. Potato slices were used as raw materials in the drying process. Experiments were carried out by varying three different hot air velocities and two different drying temperatures. It was found that under the same hot air temperature and air velocity, the change in moisture content in each drying tray and the shrinkage in each coordinate of the potato slices were different. Artificial neural network model was used to predict the moisture ratio and the area ratio of the potato slices based on the experimental data. The moisture ratio obtained from the experiment was evaluated by comparing it with the drying model. The results showed a good confidence level with the coefficient of determination in the range of 0.962 7–0.993 3. The shrinkage analysis was based on the photographic data taken through image processing before usage as the output data for the predictive model. The predictive model was designed to have various architectures with different parameters; both hidden layer and hidden layer size, learning rate, training cycles, sampling type and split ratio. The best moisture ratio and area ratio model provided the coefficient of determination of 0.996 and 0.970, respectively.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 457-475"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49485571","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":"Mask R-CNN aided fruit surface temperature monitoring algorithm with edge compute enabled internet of things system for automated apple heat stress management","authors":"Basavaraj R. Amogi ,&nbsp;Rakesh Ranjan ,&nbsp;Lav R. Khot","doi":"10.1016/j.inpa.2023.12.001","DOIUrl":"10.1016/j.inpa.2023.12.001","url":null,"abstract":"<div><div>Our prior study focused on development of internet of things (IoT) and edge-compute enabled crop physiology sensing system (CPSS) for apple sunburn monitoring. Edge compute algorithm on CPSS estimated sunburn susceptibility as fruit surface temperature (FST) through pixel-by-pixel multiplication of captured thermal infrared images with segmented fruits binary mask. The segmentation was performed using color-based K means clustering approach. This limited CPSS applicability to monitor sunburn of red colored cultivars only and when fruits develop color, typically late growing season. This is a key research gap as recent weather patterns have shown that sunburn can occur during early growing season when fruits are green to yellow. Therefore, aim of this study was to develop and field evaluate cultivar and color independent mask region-convolution neural network (R-CNN) aided fruit segmentation model and edge compute compatible FST estimation algorithm. Season long field data were collected in 2021 using eight CPSS nodes (three in cv. WA38 [Cosmic crisp] and five in cv. Honeycrisp). Collected data were used to develop and validate mask R-CNN based fruit segmentation model. Developed mask R-CNN based model was able to segment fruits of two apple cultivars and of varying colors with 91.4 % average precision. In orchard evaluations (2022 season), the resulting algorithm ported on CPSS was able to accurately segment (dice similarity coefficient = 0.89) and estimate apple FST with &lt; 0.5 °C error compared to ground truth data. With compute time of about 37 s, data processing time was reduced by 22 % over previous algorithm. High ambient temperature (&gt;35 °C) on a warmer day resulted in multiple throttling errors caused by excessive CPU temperature; however, the CPSS performance was uncompromised in FST estimation. Ambient air temperature did not affect RAM utilization and CPU clock frequency. Overall, developed FST algorithm can potentially be used as input to actuate water-based cooling system.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 603-611"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138613036","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":"Plot level sugarcane yield estimation by machine learning on multispectral images: A case study of Bundaberg, Australia","authors":"Sharareh Akbarian ,&nbsp;Mostafa Rahimi Jamnani ,&nbsp;Chengyuan Xu ,&nbsp;Weijin Wang ,&nbsp;Samsung Lim","doi":"10.1016/j.inpa.2023.06.004","DOIUrl":"10.1016/j.inpa.2023.06.004","url":null,"abstract":"<div><div>Early crop yield prediction provides critical information for Precision Agriculture (PA) procedures, policymaking, and food security. The availability of Remote Sensing (RS) datasets and Machine Learning (ML) approaches improved the prediction of sugarcane crop yield on the local and global scales, but an additional effort on the plot scale prediction is required. Challenges for plot-level prediction include a high ratooning capacity of the sugarcane crop, the lack of high spatial resolution data during the critical growth stages, and the non-linear complexation of yield data. The principal objective of the study is to analyse the potential of a time series of high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery along with three advanced ML techniques, namely Random Forest Regression (RFR), Support Vector Regression (SVR), and Nonlinear Autoregressive Exogenous Artificial Neural Network (NARX ANN) as a solution to the plot-level sugarcane yield prediction. An experimental sugarcane field containing 48 plots was selected, and UAV imagery was collected during the three consecutive cropping seasons' early and middle crop growth stages. Each dataset per growth stage was analyzed separately to predict the sugarcane crop yield in an attempt to discover how early the prediction of pre-harvest yield can be achieved. The datasets of the first two cropping seasons were trained and tested using the three ML techniques, utilizing 10-fold cross-validation to avoid overfitting. The third cropping season dataset was then used to evaluate the reliability of the developed prediction models. The results show that the correlation of Vegetation Indices (VIs) with crop yield in the middle stage outperforms the early stage in all three ML models. Moreover, comparing these models indicates that the NARX ANN method outperformed the others in the middle stage with the highest correlation coefficient (R²) of 0.96 and the lowest Root Mean Square Error (RMSE) of 4.92 t/ha. It was followed by the SVR (R² = 0.52, RMSE of 14.85 t/ha), which performed similarly to the RFR method (R² = 0.48, RMSE = 11.20 t/ha). In conclusion, the best-suited model for predicting sugarcane yields during the middle growth stage is a NARX ANN model employing the Normalized Difference RedEdge (NDRE), which demonstrates the feasibility of the ML approaches to predict the plot level sugarcane yield at a specific period of growth as they are less sensitive to the inconsistency of data collection times.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 476-487"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48925313","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 new greenhouse energy model for predicting the year-round interior microclimate of a commercial greenhouse in Ontario, Canada","authors":"Alex Nauta,&nbsp;Jingjing Han,&nbsp;Syeda Humaira Tasnim,&nbsp;William David Lubitz","doi":"10.1016/j.inpa.2023.06.002","DOIUrl":"10.1016/j.inpa.2023.06.002","url":null,"abstract":"<div><div>Modelling the energy use and microclimate of a greenhouse can be a valuable tool for commercial growers, making it possible to predict the impact of making changes to greenhouse systems and operation. This allows energy saving scenarios to be identified and can reduce energy use costs. In this study, a lumped capacitance thermal model is developed to simulate the greenhouse interior microclimate based on exterior conditions and operating settings. The current study incorporated many aspects of a complex commercial greenhouse not commonly seen in literature, such as evaporative cooling pads, dehumidification technology, gas burners, energy curtains, supplementary heating and lighting, and forced ventilation. The model was successfully validated at multiple greenhouse sections of a commercial greenhouse during spring, summer, and fall conditions in the southern Ontario climate. Data was collected from the greenhouse from March to November of 2019 at 15-minute intervals. The measured interior temperature and relative humidity data were used to evaluate the accuracy of the model simulations, while other measurements, such as solar radiation, were used as model inputs. The study greenhouse was unique, as potted rose crops were cycled between sections during the growth stage. This made variation in plant properties relatively small during the different seasons. Detailed information on the model methodology was included to improve reader’s understanding. Overall, the model accuracy is comparable or even better when compared to similar models in the literature, proving it is versatile and can be used as a design tool moving forward. In the future, the current model will be used to conduct comparative analyses of a range of different energy-use reduction technologies and operating procedures (including year-round production) to quantify the most economically and practically feasible options specifically for Ontario greenhouse growers.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 438-456"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43514932","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 review on beef cattle supplementation technologies","authors":"Guilherme Defalque ,&nbsp;Ricardo Santos ,&nbsp;Marcio Pache ,&nbsp;Cristiane Defalque","doi":"10.1016/j.inpa.2023.10.003","DOIUrl":"10.1016/j.inpa.2023.10.003","url":null,"abstract":"<div><div>The increase in the worldwide population reflects the expansion of beef cattle production and exportation. Although pasture is the world’s primary feed source of cattle food, failures in pasture management can endanger the productivity of beef cattle. An option for reducing the issues brought on by a shortage of nutritional resources and maintaining the fodder pasture is to perform the supplementation process on the livestock, even being one of the most costly activities in animal management. To decrease expenses and the need for labor to supplement the herd and improve animal performance, many parameters directly associated with supplementation must be monitored, such as environmental climate, soil and pasture characteristics, animal welfare, weight, and health. With so many parameters that impacts the decision on the quality and quantity of supplement to be supplied to the herd, sensors, remote sensing, and agricultural machinery are essential. The joint usage of these technologies in the supplementation process is complex, and there is a gap in decision-making systems for dynamic supplementation. Therefore, this work aims to carry out a comprehensive literature review that characterizes the main technologies related to the bovine supplementation process, mapping the main processes that involve the use of technological tools in the most diverse application domains. Finally, we propose a new Internet of Things architecture focused on the cattle supplementation process that combines technologies to compose a dynamic supplementation decision-making system capable of estimating the quantity and quality of the supplement that the herd needs in the presence of changes in the environment, pasture, and animals’ conditions parameters to reach production targets.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 559-572"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455187","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 tree ring detection of common Indiana hardwood species through deep learning: Introducing a new dataset of annotated images","authors":"Fanyou Wu ,&nbsp;Yunmei Huang ,&nbsp;Bedrich Benes ,&nbsp;Charles C. Warner ,&nbsp;Rado Gazo","doi":"10.1016/j.inpa.2023.10.002","DOIUrl":"10.1016/j.inpa.2023.10.002","url":null,"abstract":"<div><div>Tree-ring dating enables gathering necessary knowledge about trees, and it is essential in many areas, including forest management and the timber industry. Tree-ring dating can be conducted on either wood’s clean cross-sections or tree trunks’ rough end cross-sections. However, the measurement process is still time-consuming and frequently requires experts who use special devices, such as stereoscopes. Modern approaches based on image processing using deep learning have been successfully applied in many areas, and they can succeed in recognizing tree rings. While supervised deep learning-based methods often produce excellent results, they also depend on extensive datasets of tediously annotated data. To our knowledge, there are only a few publicly available ring image datasets with annotations. We introduce a new carefully captured dataset of images of hardwood species automatically annotated for tree ring detection. We capture each wood cookie twice, once in the rough form, similar to industrial settings, and then after careful cleaning, that reveals all growth rings. We carefully overlap the images and use them for an automatic ring annotation in the rough data. We then use the Feature Pyramid Network with Resnet encoder that obtains an overall pixel-level area under the curve score of 85.72% and ring level <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> score of 0.7348. The data and code are available at <span><span>https://github.com/wufanyou/growth-ring-detection</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 552-558"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455319","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}