Computers and Electronics in Agriculture最新文献

{"title":"Parameter optimization and numerical analysis of the double disc digging shovel for corn root-soil complex","authors":"Hailong Che ,&nbsp;Hua Zhou ,&nbsp;Yinping Zhang ,&nbsp;Zhengzhong Li ,&nbsp;Xian Wang ,&nbsp;Jiaye Li ,&nbsp;Jinli Chen ,&nbsp;Hui Zhou","doi":"10.1016/j.compag.2025.110386","DOIUrl":"10.1016/j.compag.2025.110386","url":null,"abstract":"<div><div>Under conservation tillage conditions, the presence of corn stubble has become the main obstacle affecting no-till planting. To address this issue, we propose a method of digging followed by processing. A double disc digging shovel (D-D-S) is designed while minimizing soil disturbance. By constructing a kinematic model, the mathematical relationship between its motion characteristics and key parameters is revealed. Furthermore, in-depth analysis is conducted on the mechanical behavior of D-D-S, and corresponding mechanical model is established based on this. The results of the parameter optimization tests show that when the horizontal deflection angle of the discs is 31°, the vertical deflection angle is 27°, and the spacing is 0.5 cm, the relative errors of forward displacement of the corn root-soil complex (CRSC), longitudinal displacement of CRSC, soil disturbance area, and draught force compared to the predicted values are 8.2 %, 9.3 %, 9.6 %, and 6.8 %, respectively. The movement of particles during the digging and throwing process of the D-D-S is analyzed using DEM. The D-D-S can not only dig the CRSC and lift it to an appropriate height but also backfill a certain amount of the digging soil. Comparisons between simulation test and validation test show that the soil disturbance area of both methods differ by only 7.84 %. The relative errors of the forward and longitudinal displacements of the CRSC are 8.52 % and 4.05 %, respectively, and the verification test and simulation results are basically consistent. The developed D-D-S is of significant importance for the treatment of corn stubble under conservation tillage conditions.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110386"},"PeriodicalIF":7.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Appearance quality identification and environmental factors tracing of Lyophyllum decastes for precise environment control using knowledge graph","authors":"Kai Zhou ,&nbsp;Junyuan Yu ,&nbsp;Haotong Shi ,&nbsp;Rui Hou ,&nbsp;Huarui Wu ,&nbsp;Jialin Hou","doi":"10.1016/j.compag.2025.110369","DOIUrl":"10.1016/j.compag.2025.110369","url":null,"abstract":"<div><div>In the factory production of <em>Lyophyllum decastes</em>, inappropriate cultivation environments can lead to appearance quality issues, which in turn affect both yield and quality. However, the appearance characteristics of <em>Lyophyllum decastes</em> influenced by environmental factors share similarities, and the environmental factors that cause appearance quality problems exhibit coupling and complexity. Therefore, the identification of appearance characteristics and tracing of environmental factors present significant challenges. To address this issue, this paper proposes a multimodal learning network, DCRes-GAT, which integrates an improved Residual Neural Network (DCResNet) and a Graph Attention Network (GAT) to accurately identify the features of <em>Lyophyllum decastes</em>, while simultaneously tracing environmental factors and providing control recommendations. First, a knowledge graph based on the prior knowledge of quality and environmental factors is constructed, mapping this information to a point space and extracting key features. Next, DCResNet is employed to extract optical features from <em>Lyophyllum decastes</em> images. In addition, the receptive field is expanded through dilated convolutions, while pixel-level details are preserved, and a Convolutional Block Attention Module (CBAM) is incorporated to identify subtle visual differences. Finally, a dot product operation fuses point-space features with visual features, achieving accurate identification of characteristics and providing suggestions. Experimental results demonstrate that the DCRes-GAT model performs excellently, with a feature identification accuracy of 99.45%, and can precisely diagnose key environmental factors that cause appearance quality problems, achieving a diagnostic accuracy of 99.84%. This provides a basis for the precise control of the cultivation environment of <em>Lyophyllum decastes</em>.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and tracking of agricultural spray droplets using GSConv-enhanced YOLOv5s and DeepSORT","authors":"Chen Shengde ,&nbsp;Liu Junyu ,&nbsp;Xu Xiaojie ,&nbsp;Guo Jianzhou ,&nbsp;Hu Shiyun ,&nbsp;Zhou Zhiyan ,&nbsp;Lan Yubin","doi":"10.1016/j.compag.2025.110353","DOIUrl":"10.1016/j.compag.2025.110353","url":null,"abstract":"<div><div>Accurate detection and tracking of agricultural spray droplets are crucial for optimizing spraying efficiency and ensuring uniform pesticide application. This study presents an improved droplet detection and tracking framework by enhancing the YOLOv5s model with GSConv technology, thereby improving droplet detection accuracy. To enhance tracking robustness, DeepSORT was integrated with Kalman filtering, effectively incorporating motion and appearance information. Experimental results demonstrate that the proposed method achieves a detection frame rate of 105 fps and an [email protected] of 0.9184, indicating high precision across different recall rates. Additionally, tracking performance was evaluated against manual droplet counting across five test videos, yielding a mean absolute percentage error (MAPE) of 6.434 %, further validating the accuracy and reliability of the system. These results highlight the potential of the proposed approach for real-time monitoring of spray quality, facilitating precise control of spraying parameters, and contributing to advancements in precision agriculture.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110353"},"PeriodicalIF":7.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive data relay transmission in wireless sensor networks for reliable crop growth monitoring","authors":"Irina Samoylenko ,&nbsp;Vladimir Fedorenko ,&nbsp;Vladimir Samoylenko","doi":"10.1016/j.compag.2025.110367","DOIUrl":"10.1016/j.compag.2025.110367","url":null,"abstract":"<div><div>The use of a Wireless Sensor Network (WSN) to monitor agricultural plantations is a crucial component in implementing smart agriculture. Strong signal attenuation in wireless communication channels is observed during the vegetation of plants. Therefore, it is important to develop recommendations for ensuring network reliability until the end of the growing season. This study provides an analytical model of energy losses during data packet transmission, factoring in technical characteristics of nodes, communication distance and the signal fading depth (Rician K-factor). To minimize energy losses, we propose adaptive data relay strategies that adjust the communication distance based on the stages of plant growth. Our findings indicate that the proposed adaptive packet relay transmission method successfully reduces node energy losses by 26% compared to traditional fixed transmission scenarios. Additionally, we introduce a criterion for optimal relay options and an algorithm for scheduling node activity modes in response to changing vegetation conditions, resulting in enhanced network reliability throughout the vegetation period. This work represents a promising avenue for future research in agricultural monitoring systems, with the potential to extend these strategies to more complex WSN configurations.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"234 ","pages":"Article 110367"},"PeriodicalIF":7.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The diversity in dairy cattle reticulorumen temperature: Identifying water intake events","authors":"A.K. Shirley ,&nbsp;P.C. Thomson ,&nbsp;A. Chlingaryan ,&nbsp;C.E.F. Clark","doi":"10.1016/j.compag.2025.110357","DOIUrl":"10.1016/j.compag.2025.110357","url":null,"abstract":"<div><div>Climate change and associated weather variability across the Australian landscape has lent themselves to an increased incidence of cattle heat stress. Water consumption can have a sizeable, sustained impact on reticulorumen temperature readings, thereby impacting our interpretation of an individual’s underlying physiological response to changing environmental conditions. To distinguish drinking events, we developed a drinking event detection model based on observed drinking events (video recording) from 28 dairy heifers, alongside sensor-derived reticulorumen temperature (smaXtec Animal Care GmbH) profiles. The optimised model identified drinking events with high accuracy (F-score = 0.99), as predicted when the average reticulorumen temperature declined by at least 0.5°C per 10-minutes, over a 10-, 20-, or 30-minute period. To account for differences in rapidity of decline, smaller reductions of 0.25°C per 10 min were considered valid indicators of a drinking event, provided the 0.5°C per 10-minute threshold was also met in a consecutive observation period. The temporal variability in drinking behaviour for 1,429 lactating dairy cattle across three dairy farms was then determined. Daily drinking events were greater in summer (mean 4.1) than winter (mean 3.3), while the change in reticulorumen temperature with each drinking event was smaller in summer (mean 3.7°C) than winter (mean 4.9°C). Drinking-recovery duration averaged 97.8 min/event. By revealing temporal differences in drinking behaviour for pasture-based dairy cattle, this work provides the basis for an improved understanding of core body temperature diversity.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110357"},"PeriodicalIF":7.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable machine learning technique to identify the key meteorological factors influencing the incidence of wheat Fusarium head blight in Korea","authors":"Noh-Hyun Lee ,&nbsp;Jung-Wook Yang ,&nbsp;Jin-Yong Jung ,&nbsp;Yul-Ho Kim ,&nbsp;Kwang-Hyung Kim","doi":"10.1016/j.compag.2025.110355","DOIUrl":"10.1016/j.compag.2025.110355","url":null,"abstract":"<div><div>Fusarium Head Blight (FHB), predominantly caused by <em>Fusarium asiaticum</em>, represents a major threat to wheat production in Korea, resulting in yield losses, severe economic consequences, and increased risks of mycotoxin-induced health disorders, including liver damage, immune dysfunction, carcinogenesis, and reproductive impairments, in both humans and animals. Nevertheless, there is a limited understanding of the growth stage-specific environmental conditions favoring FHB occurrence in wheat growing fields. In this study, we successfully applied an interpretable machine learning technique to identify the key meteorological variables influencing FHB in Korea. Nationwide FHB incidence data, collected from all wheat-growing regions between 2015 and 2021, were utilized for this analysis. Two machine learning models, Random Forest (RF) and Boosted Regression Trees (BRT), were employed because they generally exhibit lower sensitivity to correlations among variables than statistical models and require smaller datasets than deep learning methods. Using these models, we identified three key variables and their critical thresholds for FHB occurrence in Korea: a relative humidity (Rhum) of 75 % during the heading period, and an Rhum of 75 % combined with 60 mm of precipitation during the flowering period. Furthermore, exceeding two or all three of these thresholds significantly increased FHB incidence compared to exceeding only a single threshold. Overall, this study revealed the feasibility and potential applicability of interpretable machine learning techniques to better understand the relationship between disease incidence and environmental conditions not only for wheat FHB but also other plant diseases.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110355"},"PeriodicalIF":7.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-precision prototype for garlic apex reorientation based on artificial intelligence models","authors":"Luis Enrique Raya-González ,&nbsp;Víctor Alfonso Alcántar-Camarena ,&nbsp;Alberto Saldaña-Robles ,&nbsp;Edgar Francisco Duque-Vazquez ,&nbsp;Guillermo Tapia-Tinoco ,&nbsp;Noé Saldaña-Robles","doi":"10.1016/j.compag.2025.110375","DOIUrl":"10.1016/j.compag.2025.110375","url":null,"abstract":"<div><div>Sowing and harvesting are the most expensive operations in garlic cultivation (<em>Allium sativum L.</em>). For mechanized sowing to be feasible, the garlic clove must be placed in the soil with the apex pointing upwards, otherwise, yield can be reduced by up to 23%. In this context, artificial intelligence (AI) emerges as a viable solution to address these issues, particularly artificial neural networks (ANN). This research presents the development and evaluation of a garlic apex orientation device, which utilizes AI models adapted to all types of garlic clove shapes. The evaluated models are support vector machine (SVM), random forest (RF), ANN, convolutional neural network (CNN), and transfer learning (TL). To increase the number of available images for training, a generative adversarial network (GAN) was used. Three different databases were used to train models to determine achieved the best performance in terms of model accuracy. The databases used are the original database, an augmentation version of the original database incorporating images generated by the GAN model, and only images generated by the GAN model. The results show that the best model (ANN) achieves a validation accuracy of 99.74% when using an augmentation of the original database with artificial images generated by the GAN model.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110375"},"PeriodicalIF":7.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resource-constrained stereo matching for greenhouse tomatoes and napa cabbages: An efficient and accurate approach with optimized memory usage","authors":"Cheoul Young Kim ,&nbsp;Wanhee Han ,&nbsp;No-Cheol Park ,&nbsp;Soo Hyun Park","doi":"10.1016/j.compag.2025.110358","DOIUrl":"10.1016/j.compag.2025.110358","url":null,"abstract":"<div><div>This study introduces a novel stereo matching algorithm designed for agricultural applications in greenhouses. Addressing the limitations of existing convolutional neural network (CNN)-based stereo matching models, the proposed architecture enhances efficiency by converting RGB images to grayscale and applying histogram equalization. This method preserves essential visual information while reducing data size and computational complexity. Rectangle filter kernels are also used to prioritize horizontal information, aligning with the typical arrangements of stereo camera pairs. The disparity prediction model is initially trained on a subset of 29,204 image pairs from the Scene Flow dataset and subsequently retrained and evaluated using 2,470 tomato image pairs and 486 napa cabbage image pairs from Greenhouse. This further refines its performance in real agricultural settings. The proposed model surpasses existing models such as geometry and context network (GC-Net), pyramid stereo matching network (PSMNet), 2D-Mobilestereonet and 3D-Mobilestereonet in terms of disparity prediction accuracy and computational speed, consuming less than one-third of the memory. Given the unique challenges of greenhouse environments, this approach demonstrates a robust method for developing stereo matching algorithms suited for stereo vision applications in such settings.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110358"},"PeriodicalIF":7.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nighttime environment enables robust field-based high-throughput plant phenotyping: A system platform and a case study on rice","authors":"Binghui Xu ,&nbsp;Jiafei Zhang ,&nbsp;Zhixin Tang ,&nbsp;Yongshuai Zhang ,&nbsp;Lingli Xu ,&nbsp;Hao Lu ,&nbsp;Zhiguo Han ,&nbsp;Weijuan Hu","doi":"10.1016/j.compag.2025.110337","DOIUrl":"10.1016/j.compag.2025.110337","url":null,"abstract":"<div><div>Plant phenotyping has emerged as a cornerstone for deciphering the complex interplay between plant genetics and environmental factors. To acquire reliable plant phenotypes, it is important to have an accurate, robust, and high-throughput plant phenotyping system platform. During the last decade, the community seems to have a consensus that such systems should be deployed and work in daytime. Many phenotyping results, however, can be inaccurate and unstable in daytime due to rapid changes in lighting and shadows, particularly for vision-based systems. In this work, we build upon a commercial vision-based high-throughput plant phenotyping (HTPP) platform TraitDiscover and customize a nighttime working mode for the platform. In particular, we incorporate several hardware designs tailored to the nighttime environment such as the array-style lighting equipment and the three-axis high-precision automated control system. On the software side, we also integrate state-of-the-art YOLOv8 object detection and K-Net semantic segmentation frameworks to enable high-performance nighttime image analysis. The feasibility and robustness of the system are demonstrated with a case study on rice. To quantify the effectiveness of phenotyping, a high-quality nighttime rice image segmentation dataset is collected, with 360 finely annotated masks of rice plants. Experimental results show that our customized system is able to achieve surprisingly high segmentation performance up to 93.52% mask IoU, which is significantly higher than the metrics reported from daytime phenotyping. From the mage analysis results, we further extract and validate 28 phenotyping parameters related to color, morphology, and texture status. The average <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> between the inferred phenotype parameters and the actual values reached 0.95, demonstrating the reliability and robustness of the system in nighttime phenotyping. Our results and findings may encourage phenotyping practitioners to rethink the current de facto choice of deploying ‘daytime plant phenotyping platforms’.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":""},"PeriodicalIF":7.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Model prediction of plant morphology, water flows and xylem water potential in a growing tomato plant under heterogeneous growing conditions","authors":"Jakub Šalagovič ,&nbsp;Pieter Verboven ,&nbsp;Maarten Hertog ,&nbsp;Bram Van de Poel ,&nbsp;Bart Nicolaï","doi":"10.1016/j.compag.2025.110346","DOIUrl":"10.1016/j.compag.2025.110346","url":null,"abstract":"<div><div>We present a comprehensive mathematical model to calculate stem water potential in tomato plants cultivated under greenhouse conditions. Stem water potential is one of the variables that determines the growth of fruit as water potential gradients between the fruit and the stem are the driving forces for import of water and solutes into the fruit. Notably, the model integrates growth dynamics, environmental conditions, and plant management strategies to improve the accuracy of water potential estimation throughout the canopy. Environmental factors (i.e., temperature, relative humidity, light irradiance) were implemented at plant compartment levels, allowing for precise microclimate representation. Plant structure was used to calculate water flows and, ultimately, stem water potential by utilizing a hydraulic resistance model. The model was calibrated and validated using data collected from five growing seasons (2020 – 2024). The precision of water potential estimates across different growth stages was improved by including plant morphology dynamics. This, together with discretisation into compartments, allowed for unique realistic predictions for the whole season. Accurate predictions required accounting for growth dependency in root and xylem resistance. Temperature was the main predictor of plant growth for the investigated conditions of tomato production in Belgium. The greenhouse environment and plant management significantly influenced water fluxes and subsequent water potential estimations and should always be considered, especially for whole-season scenarios. Two hypothetical scenarios were analyzed based on 2019 environmental data, exploring the impact of greenhouse management and climate change. Simulations revealed that an increase in the greenhouse minimum temperature set points (+2 °C) had a greater positive effect on yield than a hypothetical climate change scenario with a larger temperature increase (+4 °C). The latter resulted in a higher prevalence of suboptimal growth conditions, presenting a real challenge for efficient future greenhouse management. Additionally, controlling the vapour pressure deficit instead of relative humidity was shown to significantly reduce water demand due to decreased transpiration rates. This water potential model for tomato growth can be used conjointly with fruit growth models for better crop prediction and optimisation of growing conditions. The presented model is modular and extendable, allowing integration not just with fruit growth models, but also potential inclusion of additional plant organs.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110346"},"PeriodicalIF":7.7,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}