Information Processing in Agriculture最新文献

{"title":"Integrating Artificial Intelligence in dairy farm management − biometric facial recognition for cows","authors":"Shubhangi Mahato ,&nbsp;Suresh Neethirajan","doi":"10.1016/j.inpa.2024.10.001","DOIUrl":"10.1016/j.inpa.2024.10.001","url":null,"abstract":"<div><div>The integration of Artificial Intelligence (AI) into dairy farm management through biometric facial recognition of cows marks a significant milestone in livestock care. This comprehensive review explores the development, implementation, and challenges associated with AI-powered biometric facial identification in dairy agriculture. It emphasizes the pivotal role of this innovation in enabling precise monitoring of individual cows, thereby facilitating thorough tracking of their health, behaviors, and productivity levels. Derived from facial recognition technologies originally designed for humans, this approach harnesses distinctive features of cow faces for gentle and immediate observation within large-scale farming operations. The evolution of AI from basic pattern recognition to advanced Convolutional Neural Networks (CNNs) and deep learning frameworks signifies a transition toward data-driven agriculture. This analysis addresses notable challenges such as environmental variability, data collection difficulties, ethical considerations, and technological limitations. Furthermore, it compares various AI frameworks, highlighting their unique advantages and suitability in the dairy farming context. Despite these obstacles, facial recognition technology holds promise for enhancing farm efficiency, improving animal welfare, and promoting sustainable practices, underscoring the need for ongoing research and innovation. We advocate for future investigations focused on enhancing adaptability to diverse environments, ensuring ethical AI deployment, fostering compatibility across different breeds, and integrating with complementary agricultural technologies. Ultimately, this review underscores the transformative impact of AI in advancing dairy farming towards a data-centric future while prioritizing responsible agricultural practices.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 3","pages":"Pages 312-325"},"PeriodicalIF":7.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145327415","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 novel method to detect stem and fruit dynamically for apricot posture estimation and adjustment","authors":"Wulan Mao ,&nbsp;Leilei He ,&nbsp;Man Xia ,&nbsp;Hanhui Jiang ,&nbsp;Rui Li ,&nbsp;Ramesh Sahni ,&nbsp;Yaqoob Majeed ,&nbsp;Zhanjiang Zhu ,&nbsp;Longsheng Fu","doi":"10.1016/j.inpa.2024.12.001","DOIUrl":"10.1016/j.inpa.2024.12.001","url":null,"abstract":"<div><div>Separating pulp and core is critical for apricot processing, but faces labor shortages. To address this challenge, a fully automated pitting machine (FAPM) based on automatic apricot orientation device (AAOD) was proposed to achieve mechanized pitting by apricot automatic orientation. The designed and constructed AAOD adopt with dynamic visual detection and mechanical orientation for apricot posture adjustment. YOLOv8 series models were applied for apricot and stem detection, and then estimating their three-dimensional posture. Compared with other YOLOv8 series models, YOLOv8n was selected as the preferred detection model with a detection speed of 10.3 ms and a size of 6.1 MB to meet the need of real-time detection and lightweight deployment. YOLOv8n achieved precision (<em>P</em>), recall (<em>R</em>), and mean average precision (<em>mAP</em>) values of 82.0 %, 90.9 %, and 90.1 %, respectively. Moreover, new indicators, namely positional offsets in the image coordinate system (<em>Offset_img</em>), positional offsets (<em>Offset_3D</em>), angular offsets in the 3D coordinate system (<em>Offset_ang</em>), and the ratio of intersection to manual bounding box areas (<em>Ratio_im</em>), were proposed to validate the performance of AAOD for position estimation in three varieties of apricot. The best performance was obtained in Saimaiti apricot and achieved <em>Offset_img</em> of 2.9 pixels, <em>Offset_3D</em> of 1.2 mm, and <em>Offset_ang</em> of 0.9°, with <em>Ratio_im</em> for apricot and stem were 99.3 % and 97.3 %. Experimental show that the optimal operating parameters for AAOD are 20 rps for alignment wheel rotation speed and the distance of 22.5 mm from apricot base to alignment wheel axis, which presented the best successful orientation rate of 91.5 % with an <em>Offset_3D</em> of 1.8 mm. Result demonstrated that the dynamic detection-based orientation approach proposed in this study has great potential for automatic apricot pitting.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 3","pages":"Pages 358-369"},"PeriodicalIF":7.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145327150","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":"Enhancing leaf disease detection accuracy through synergistic integration of deep transfer learning and multimodal techniques","authors":"Divine Senanu Ametefe ,&nbsp;Suzi Seroja Sarnin ,&nbsp;Darmawaty Mohd Ali ,&nbsp;Aziz Caliskan ,&nbsp;Imène Tatar Caliskan ,&nbsp;Abdulmalik Adozuka Aliu ,&nbsp;Dah John","doi":"10.1016/j.inpa.2024.09.006","DOIUrl":"10.1016/j.inpa.2024.09.006","url":null,"abstract":"<div><div>The agricultural sector, a cornerstone of economies worldwide, faces significant challenges due to plant diseases, which severely affect crop yield and quality. Early and accurate detection of these diseases is crucial for effective mitigation strategies. The current methods used often lack accuracy and adaptability, especially in diverse environmental conditions. This study introduces a novel, synergistic approach that integrates deep transfer learning with multimodal techniques, specifically canny edges, colour spectrum intensity analysis, and custom data augmentation strategies. Unlike existing methods that rely solely on pre-trained models, the approach utilised in this study offers an innovative fusion of distinct feature extraction techniques. The canny edges highlighted the structural intricacies of leaf diseases, while colour spectrum intensity analysis enhanced the detection of disease-specific colour markers. The customized data augmentation techniques employed (in the study) was shown to enhance the learning process of the models, resulting in their adaptability to diverse agricultural environments. This integration applied to DenseNet201 and EfficientNetB3, achieved detection accuracies of 99.03 % and 98.23 %, respectively, surpassing traditional models and setting new benchmarks in plant disease detection. These results demonstrate the effectiveness of the proposed multi-faceted approach and its potential to significantly enhance crop disease management systems.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 3","pages":"Pages 279-299"},"PeriodicalIF":7.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145327146","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":"Leveraging data from plant monitoring into crop models","authors":"Monique Pires Gravina de Oliveira ,&nbsp;Thais Queiroz Zorzeto-Cesar ,&nbsp;Romis Ribeiro de Faissol Attux ,&nbsp;Luiz Henrique Antunes Rodrigues","doi":"10.1016/j.inpa.2025.02.003","DOIUrl":"10.1016/j.inpa.2025.02.003","url":null,"abstract":"<div><div>An increase in data availability from different sensors and sources has changed how crop models are being used. Data assimilation is one approach for integrating data and models that has been widely used for field crops but not yet in protected environments. We present a case study of data assimilation in a greenhouse, updating growth estimates of the Reduced State TOMGRO model. We assimilated data obtained through the continuous monitoring of plant mass and images captured by low-cost cameras, using the Unscented Kalman Filter and the Ensemble Kalman Filter. In some cases, assimilation led to improvements of more than 40% in the RMSE of yield estimates of the non-calibrated model, within a validation set. The improvements were more noticeable when there was a need to adjust the estimates to a condition the model does not represent. In these situations, we noted the RMSE decreased by almost 80%, depending on the variable being assimilated. However, in some cases, the results were also impaired by assimilation, and we highlight the impacts on the filter performance caused by the quality of observations and of observation models. Overall, the employed measurements, i.e., area of organs observed in pictures and plant-water mass, seemed suitable for tracking plant growth and for obtaining good approximations of the state variables estimated by the model. As with other studies, it was not the case that assimilating one state was useful for improving the value of others, including yield. As the first study using filters and non-destructive observations in a process-based crop model in a protected environment, we identified a lot of potential, but to identify the best use of these techniques with real-time data, more studies are needed. By making all data and code from this study available, we hope to ease future research in this area.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 3","pages":"Pages 408-429"},"PeriodicalIF":7.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145327148","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 novel random forest-based approach for the non-destructive and explainable estimation of ammonia and chlorophyll in fresh-cut rocket leaves","authors":"Stefano Polimena ,&nbsp;Gianvito Pio ,&nbsp;Maria Cefola ,&nbsp;Michela Palumbo ,&nbsp;Michelangelo Ceci ,&nbsp;Giovanni Attolico","doi":"10.1016/j.inpa.2024.09.002","DOIUrl":"10.1016/j.inpa.2024.09.002","url":null,"abstract":"<div><div>The perceived visual quality of fruits and vegetables plays a central role in the choices made by retail customers. Machine learning (ML) approaches based on image analysis have been recently proposed to overcome the poor efficiency and subjectivity of human visual evaluation as well as the expensiveness and destructiveness of physical and chemical methods that measure internal indicators. In this paper, we propose a ML method based on Random Forests for estimating the chlorophyll and ammonia contents (considered, in the literature, reliable indicators of product freshness) from images of fresh-cut rocket leaves. Our approach copes with specific issues raised by (i) the non-uniform distributions of ammonia and chlorophyll values and (ii) the need to provide insights into the features that produce a particular model outcome, aiming to enhance its trustworthiness. Our experiments, performed on real images of fresh-cut rocket leaves, proved that the proposed approach significantly outperforms 7 competitor methods, obtaining an improvement of the RSE results of 6.6% for the prediction of the ammonia and of 10.4% for the prediction of the chlorophyll over its best competitor. Moreover, a specific analysis of the explainability of the predictions showed that the learned models are based on reasonable features, empowering their acceptance in real-world applications.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 221-231"},"PeriodicalIF":7.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of cucumber downy mildew spores based on improved YOLOv5s","authors":"Chen Qiao ,&nbsp;Kaiyu Li ,&nbsp;Xinyi Zhu ,&nbsp;Jiaping Jing ,&nbsp;Wei Gao ,&nbsp;Lingxian Zhang","doi":"10.1016/j.inpa.2024.05.002","DOIUrl":"10.1016/j.inpa.2024.05.002","url":null,"abstract":"<div><div>Cucumber downy mildew is caused by the infection of leaves with downy mildew spores. However, research on the prevention and control of cucumber downy mildew often focuses on the stage after symptoms have appeared on the leaves, that is, once disease spots have already formed. Since the occurrence of downy mildew is closely related to the quantity of spores, early-stage research on the quantity of downy mildew spores is of great significance for the prevention and control of cucumber downy mildew. Consequently, developing a rapid, accurate, and efficient method for detecting cucumber downy mildew spores is critical for advancing disease control. This study introduces an improved YOLOv5s model for spore detection. The model incorporates a transformer module into YOLOv5s’s backbone, enhancing global feature information extraction. It also adds a small object detection head to counter YOLOv5s’s extensive down-sampling and difficulty in learning features of small objects. Integration with the Convolutional Block Attention Module (CBAM) further refines detection precision for small objects like mildew spores. Upon evaluation with an image dataset collected through a microscope, the improved YOLOv5s model demonstrated superior performance metrics across various resolutions. At a resolution of 1440px × 1440px, it achieved the highest mean Average Precision ([email protected]) of 95.4 %, a precision (P) score of 89.1 %, and a recall (R) rate of 90.3 %. These metrics surpassed the original YOLOv5s model at the same 1440px × 1440px resolution by 1.6 % in [email protected], 1.6 % in P, and 0.5 % in R. Additionally, the model’s [email protected] across various resolution scales indicates superior detection precision compared to other leading models like YOLOv7. In the context of microscopic images with small spores and complex backgrounds, the improved YOLOv5s model effectively detects cucumber downy mildew spores, offering valuable insights and technical support for advancing the prevention and control measures against cucumber downy mildew.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 179-194"},"PeriodicalIF":7.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115958","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":"Machine learning enabled assessment of the vacuum freeze-drying of the kiwifruit","authors":"Uzair Sajjad ,&nbsp;Farzana Bibi ,&nbsp;Imtiyaz Hussain ,&nbsp;Naseem Abbas ,&nbsp;Muhammad Sultan ,&nbsp;Hafiz Muhammad Asfahan ,&nbsp;Muhammad Aleem ,&nbsp;Wei-Mon Yan","doi":"10.1016/j.inpa.2024.09.004","DOIUrl":"10.1016/j.inpa.2024.09.004","url":null,"abstract":"<div><div>Drying technologies have been essential for extending the shelf-life of perishable fruits and vegetables for over a century. Vacuum freeze-drying (VFD), though invented over a hundred years ago, remains one of the most advanced drying techniques, known for sustainably drying perishable products while maintaining quality indices and morphological properties comparable to their fresh state. The performance of the VFD system is sensitive to the operating conditions and features of the drying product which is assessed using experimental and/or numerical methods. However, the qualitative aspects of the dried product are not predictable. In this context, the present study aims to create a deep neural framework (DNF) that predicts the performance of a Vacuum Freeze Drying (VFD) system for kiwifruit, based on its morphology and nutritional value under varying conditions. This involves translating the fruit’s morphological features into trainable data and using a Generative Adversarial Network (GAN) to create diverse, unlabeled datasets. The framework is optimized using Gaussian Process (GP) for hyper-parameter tuning, focusing on minimizing errors like mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The maximum MSE of 1.243 is found in the prediction of rehydration rate, followed by color (0.725), energy consumption (0.426), moisture content (0.379), texture (0.320), sensory (0.250), and Brix (0.215), respectively. The maximum MAE and MAPE values are recorded 0.833 and 32.99 % while the minimum is observed 0.368 and 7.019 % in the case of rehydration rate and Brix, respectively. Overall, the R² value was computed 0.863 which is reasonable for the quality assessment of kiwifruit dried by the VFD system.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 245-259"},"PeriodicalIF":7.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115864","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":"Disturbance rejection control of the agricultural quadrotor based on adaptive neural network","authors":"Wenxin Le ,&nbsp;Pengyang Xie ,&nbsp;Jian Chen","doi":"10.1016/j.inpa.2024.05.001","DOIUrl":"10.1016/j.inpa.2024.05.001","url":null,"abstract":"<div><div>In order to solve the problem of stability of agricultural quadrotor working, its controller designing is the first priority. Therefore, this paper makes an attempt to use the Radial Basis Function (RBF) neural network adaptive method combined with sliding mode control to control its height channel. Validation of the efficacy of the RBF neural network in control is conducted through simulation experiments utilizing quadrotor parameters. The application of the method to the control of agricultural quadrotor has laid a theoretical foundation. At the same time, through simulation experiments, it is concluded in theory that the RBF neural network can have a good prediction and elimination effect on the interference during the flight, and the change of the time constant will not affect the control effect of the aircraft. Notably, abrupt changes in time constant indicate UAV motor malfunction. Simulation results affirm the efficacy of the proposed control method in regulating UAV altitude and addressing sudden faults. Real-world experimentation (vegetable field including bean, pepper, eggplant, tomoto, etc.) reveals that even when UAV propellers sustain damage to a certain extent, altitude control and hover capabilities remain intact. These findings provide a solid groundwork for subsequent altitude control endeavors in agricultural quadrotor operations, while also offering innovative avenues for advancing the field.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 169-178"},"PeriodicalIF":7.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141023570","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":"Maintaining environmental context and geoprivacy protection in agriculture","authors":"Parvaneh Nowbakht ,&nbsp;Lilian O’Sullivan ,&nbsp;David P. Wall ,&nbsp;Paul Holloway","doi":"10.1016/j.inpa.2024.09.001","DOIUrl":"10.1016/j.inpa.2024.09.001","url":null,"abstract":"<div><div>To achieve sustainable agriculture and food security there is an urgent need to share agricultural data with a range of relevant stakeholders; however, to reduce the risk of identification, spatial data must be obfuscated prior to sharing. To-date, most obfuscation methods that have been developed do not consider a) the areal nature of field-level data and b) the differing environmental conditions at the original and obfuscated sites. To address these issues, we developed the Polygon-based Environmental Similarity Obfuscation Method (PESOM) to provide geoprivacy protection and guarantee that obfuscated data will retain the same environmental conditions as the original data. PESOM was developed using an unsupervised clustering algorithm and seasonal climate data, before being applied to the Nutrient Management Plan (NMP) online in Ireland. PESOM satisfied high level of geoprivacy protection and absolute environmental clustering preservation, with no false-identification and non-unique obfuscation risk. It provided a low level of distribution preservation and correlation preservation, large location displacement and subsequently low local analytical accuracy. PESOM is a significant advance on existing obfuscation techniques in agriculture data and will allow the sharing of data to be used widely for agri-environmental purposes, a current limitation of existing methods. The results of this research should be of wide interest to those working in agri-environmental research and computer science, and be of relevance to researchers, data managers, and practitioners.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 209-220"},"PeriodicalIF":7.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144115960","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":"GIS spatial optimization for agricultural crop allocation using NSGA-II","authors":"Tipaluck Krityakierne ,&nbsp;Pornpimon Sinpayak ,&nbsp;Noppadon Khiripet","doi":"10.1016/j.inpa.2024.04.005","DOIUrl":"10.1016/j.inpa.2024.04.005","url":null,"abstract":"<div><div>This study focuses on the shift from traditional farming methods, reliant on farmer intuition and manual processes, to modern, automated approaches crucial for Thailand’s agricultural sustainability. Despite its vital role in the country’s economy, outdated practices lead to supply imbalances and perpetuate poverty among smallholder farmers. Using geographic information systems (GIS) and mathematical optimization, the present study aims to determine optimal agricultural crop allocation. A multi-objective optimization crop spatial allocation model leverages geospatial data, including crop, soil and climate suitability, to enhance the accuracy of our model. Additionally, we incorporate agricultural economics data, such as market price, crop yield, production cost, distances to secondary producers, production budget limitations, and minimum crop production requirements. To speedup the convergence of the algorithm, we introduce more suitable crossover and mutation operators in NSGA-II, aiming to direct the search towards the Pareto optimal solutions. We demonstrate the effectiveness of our approach in a case study of the agricultural area in Chiang Mai province, Thailand, focusing on three major industrial crops: corn, cane, and rice. Our model suggests land allocation that adheres to both the budget constraint and the minimum production requirements, while retaining only a small surplus for each crop. The successful implementation of this approach in our case study marks a significant advancement in Thai agricultural research, paving the way for long-term economic and environmental sustainability.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"12 2","pages":"Pages 139-150"},"PeriodicalIF":7.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140783666","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}