Smart agricultural technology最新文献

{"title":"Deep ensemble learning for weed risk mapping: Hybrid RF–CatBoost and CNN–XGBoost algorithms for predicting Chenopodium album distribution in rapeseed fields","authors":"Emran Dastres ,&nbsp;Mohsen Edalat","doi":"10.1016/j.atech.2025.101731","DOIUrl":"10.1016/j.atech.2025.101731","url":null,"abstract":"<div><div>The escalating spread of the competitive weed <em>Chenopodium album</em> poses a substantial threat to strategic oilseed production, necessitating advanced, spatially explicit risk-assessment tools. This study develops and compares two hybrid machine-learning architectures, the RF–CatBoost stacked ensemble and the CNN–XGBoost deep ensemble, to model habitat suitability of <em>C. album</em> across the heterogeneous rapeseed-growing landscapes of Fars Province, Iran. Using 18 optimized environmental covariates combined with extensive field observations, both models delivered strong predictive performance (AUC ≥ 0.82), outperforming conventional classifiers. The RF–CatBoost hybrid demonstrated the highest accuracy (AUC = 0.84 ± 0.03; TSS = 0.68 ± 0.04; Kappa = 0.63 ± 0.05) and the most stable spatial behavior (σ = 0.069; CV = 8.1%), whereas the CNN–XGBoost model showed moderately lower accuracy (AUC = 0.82 ± 0.04; TSS = 0.65 ± 0.05) with higher spatial uncertainty (σ = 0.078). Variable-importance analyses indicated that habitat suitability is primarily governed by edaphic factors, especially Clay content, Organic Matter, and Phosphorus, alongside anthropogenic dispersal pathways including proximity to roads and rivers. The resulting Habitat Suitability Maps (HSMs) reveal substantial areas of High and Very High suitability, accounting for 22.9% of the province under the RF–CatBoost model, thus offering a robust evidence base for Precision Weed Management (PWM). Overall, the findings demonstrate the effectiveness of deep ensemble learning in producing reliable ecological risk assessments and highlight priority zones where targeted management can enhance the sustainability and economic efficiency of rapeseed production in semi-arid regions.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101731"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925830","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":"An adaptive self-leveling chassis system for improving the adaptability of sugarcane harvesters on hilly terrains","authors":"Guang Liu,&nbsp;Tao Wu,&nbsp;Qingting Liu,&nbsp;Jigui Pang,&nbsp;Jinyou Zhang,&nbsp;Weijin Pan,&nbsp;Xiaoping Zou","doi":"10.1016/j.atech.2025.101706","DOIUrl":"10.1016/j.atech.2025.101706","url":null,"abstract":"<div><div>To address the problems of excessive body tilt and potential safety risks encountered by sugarcane harvesters operating on sloped terrain, this study developed an automatic self-leveling chassis to improve operational stability and terrain adaptability. The system is based on hydraulic lifting and leveling principles, achieving real-time attitude adjustment through coordinated control of multiple hydraulic cylinders. A combined approach of mathematical modeling and simulation verification was employed to investigate key parameters, including the lateral and longitudinal rollover threshold angles and the minimum turning radius. The research combined theoretical analysis with experimental validation. Results from dynamic leveling tests indicate that, when the composite mode integrating active leveling and hydraulically interconnected suspension(HIS) was activated, the steady-state errors for lateral and longitudinal leveling were 0.10° and 0.13°, respectively. Under field conditions on uneven terrain, the chassis maintained a tilt angle within ±2° These results verify the feasibility and effectiveness of the proposed leveling system and demonstrate its potential for enhancing the operational safety of sugarcane harvesters on hilly terrains. This study provides a useful reference for the development of leveling technologies in similar types of agricultural machinery.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101706"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790770","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":"Effect of spraying drone flight parameters on vineyard canopy coverage and droplet deposition","authors":"Vasilis Psiroukis ,&nbsp;Aikaterini Kasimati ,&nbsp;Konstantinos Nychas ,&nbsp;Konstantinos Dagres ,&nbsp;George Papadopoulos ,&nbsp;Evangelos Anastasiou ,&nbsp;Spyros Fountas","doi":"10.1016/j.atech.2025.101730","DOIUrl":"10.1016/j.atech.2025.101730","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) are increasingly promoted as alternatives to conventional plant protection product (PPP) spraying in vineyards, yet limited evidence exists on how flight parameters and application configurations influence spray deposition under real vineyard conditions, especially within Europe. This study evaluated the performance of a DJI Agras T16 spraying drone across eight operational configurations combining two flight altitudes (2.0 and 2.5 m AGL), two flight speeds (1.0 and 1.5 m/s), and two aircraft positioning strategies (over-row and inter-row). Spray deposition, canopy coverage, and ground losses were quantified using water-sensitive papers (WSPs) positioned at multiple canopy heights and ground locations, while meteorological conditions were monitored, following methodologies adapted from the ISO 22,866/22,522 protocols. Results showed strong interactions among altitude, speed, flight path, and wind parameters, with over-row treatments concentrating deposition in upper canopy layers and inter-row treatments producing more homogeneous profiles but higher sensitivity to wind direction. Lower altitude flights (2.0 m) combined with slower speed (1 m/s) substantially increased ground deposition regardless of UAV positioning, whereas higher altitude (2.5 m) and speed values (1.5 m/s) reduced spray losses to the ground. The increase in pump output associated with higher speed under a constant application rate is expected to produce finer droplets, which can enhance penetration but may also elevate in-field drift risk. These findings demonstrate that UAV spraying performance depends on integrated optimisation of operational settings and environmental conditions. The results provide practical guidance for improving drone-based vineyard spraying and highlight the need for updated EU regulatory frameworks tailored to UAV application characteristics.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101730"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790771","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":"Precision weed detection using UAVs and deep learning: Models, paradigms, and challenges","authors":"Utsav Bhandari,&nbsp;Aaron Etienne","doi":"10.1016/j.atech.2025.101656","DOIUrl":"10.1016/j.atech.2025.101656","url":null,"abstract":"<div><div>Weeds continue to pose a major challenge to agricultural productivity, contributing to an estimated 31.5 % yield reduction and a global economic loss of $32 billion. Recent advancements in artificial intelligence, particularly deep learning (DL), have enabled the development of precise and automated weed detection systems. This review explores the state-of-the-art in deep learning-based weed detection, with a focus on unmanned aerial vehicles (UAVs), imaging sensors, and computer vision techniques. UAVs, equipped with various imaging sensors, offer high-resolution imagery for site-specific weed monitoring. DL models including one-stage detectors such as YOLO, RetinaNet, EfficientDet, and CenterNet, and two-stage detectors like Faster R-CNN and Mask R-CNN have demonstrated strong performance in weed localization and classification tasks. While one-stage models provide faster inference for real-time applications, two-stage models typically offer higher accuracy at the cost of increased computational demands. Segmentation models such as U-Net and DeepLab further enable pixel-level weed mapping, crucial for estimating coverage and guiding targeted interventions. This paper also outlines the complete training pipeline for DL models, including data acquisition, annotation, preprocessing, model selection, and evaluation using metrics like precision, recall, mAP, and inference time. Additionally, the role of supervised, semi-supervised, and self-supervised learning paradigms is discussed in addressing data scarcity and annotation challenges. Future research should focus on improving model generalization, enabling edge deployment for real-time field applications, and developing robust, diverse datasets. These advancements will facilitate the scalable adoption of precision weed detection systems, supporting more sustainable and efficient agricultural practices.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101656"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685556","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":"Estimation of aboveground biomass in non-uniformly planted rice using multi-temporal UAV imagery data","authors":"Yong Su ,&nbsp;Jinyan Tian ,&nbsp;Bo Yang ,&nbsp;Jing Yang","doi":"10.1016/j.atech.2025.101680","DOIUrl":"10.1016/j.atech.2025.101680","url":null,"abstract":"<div><div>Estimating rice aboveground biomass (AGB) in unevenly planted areas using sowing parameters is challenging, mainly due to row spacing errors caused by the subjectivity of operators, as well as problems such as missing seeding and multiple seedlings per hill. Estimating regional rice AGB in actual agricultural production under the assumption of uniform planting density significantly increases errors. This study innovatively proposes a Rice Pixel Ratio (RPR) based on high-resolution Unmanned Aerial Vehicle (UAV) imagery to characterize the spatial distribution pattern of rice at the pixel scale. During 2023–2024, this study set up a total of 6 experimental treatments: 4 uniform planting density treatments (15, 18, 21, and 24 plants/m², sequentially denoted as K1-K4), 1 uneven density treatment (D1), and 1 composite uniform density treatment (D2) integrating K1-K4. The study revealed: (i) Eight VIs extracted from high-resolution remote sensing imagery exhibited significant correlations with real-time rice growth parameters; (ii) During the vegetative phase, after RPR calibration, the non-uniform density plot D1 showed significantly improved AGB estimation accuracy, with R² increasing from 0.76 to 0.82; (ii) When RPR&gt;0.985 in test plots, and no model accuracy degradation occurred, confirming RPR’s broad applicability and strong robustness; (iii) For the composite density model D2 incorporating all four densities, RPR calibration elevated the reproductive-phase stem-leaf model’s R² from 0.28 to 0.71, representing a 158.18 % improvement. Results demonstrate RPR’s capacity to significantly enhance the generalizability and robustness of AGB models under high vegetation coverage, mixed-density, and non-uniform planting conditions.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101680"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685591","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":"OysterMushNet: A deep learning-based mobile application for real-time mushroom disease detection and agronomist support","authors":"Deepesh Prakash Guragain ,&nbsp;Bijaya Shrestha ,&nbsp;Iswor Bajracharya ,&nbsp;Neelesh Sharma ,&nbsp;Dipesh Ghimire ,&nbsp;Showkat Ahmad Bhat","doi":"10.1016/j.atech.2025.101735","DOIUrl":"10.1016/j.atech.2025.101735","url":null,"abstract":"<div><div>Oyster mushroom cultivation is gaining popularity in Nepal because of its high nutritional value, low investment requirements, and favorable climatic conditions. However, frequent disease outbreaks significantly impact yield and farmer income. This study presents OysterMushNet, an end-to-end mobile application tailored for real-time disease classification, remedy recommendations, and agronomist support. A self-collected dataset of 8884 images from multiple farms was used to train and validate an ensemble deep-learning model that combined EfficientNetB0, DenseNet201, and ResNet50V2. The ensemble model achieved 98.8 % accuracy and a mean F1-score of 0.98, outperforming individual models. To enhance real-world usability, the model was optimized for mobile device deployment using quantization techniques. This technique significantly reduces the model memory footprint as well as the inference time. The application features bilingual support (English/Nepali) and real-time farmer-agronomist communication. Field testing with 50 farmers and 10 agronomists over six months demonstrated the application's high usability, with 94 % of users finding bi-directional communication essential and 90 % validating remedy recommendations. Additionally, farmers reported a 20–30 % reduction in crop loss due to early disease detection and expert intervention. These findings highlight the potential of OysterMushNet as a scalable AI-driven solution for precision agriculture in regions with limited access to agronomists. Future works aim to expand the dataset, improve model interpretability, and integrate additional features, such as market price tracking and video upload, to enhance the application's usability and overall impact.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101735"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925496","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":"Dual-domain Context-aware network in time series classification for identifying agricultural machinery trajectory operation mode","authors":"Weixin Zhai ,&nbsp;Le Chen ,&nbsp;Jinming Liu ,&nbsp;Zhou Guo ,&nbsp;Qian Qian ,&nbsp;Jiawen Pan ,&nbsp;Caicong Wu ,&nbsp;Xiangzeng Kong","doi":"10.1016/j.atech.2025.101774","DOIUrl":"10.1016/j.atech.2025.101774","url":null,"abstract":"<div><div>Agricultural machinery trajectory operation mode identification aims to determine the spatiotemporal distribution characteristics of agricultural machinery trajectories, thereby understanding the machinery's activity at each time step. It is a multivariate time series classification task. However, existing methods are limited to capturing feature interactions only within local trajectory points, without fully utilizing the global contextual information hidden within the trajectory. To effectively model the contextual information of trajectories to accurately identify operation modes pattern of agricultural machinery, we propose a dual-domain context-aware network (DCANet). First, we propose a multi-angle feature enhancement method that uses physical kinematics formulas and mathematical statistics methods to expand the feature set of trajectories, thereby fully exploiting the inherent information. We then design a frequency-domain context-aware module consisting of two components: a local awareness bottleneck module and a learnable frequency-domain attention context module. The former is used to model the local dependencies. Based on this, the latter uses a discrete wavelet transform and a multi-head attention mechanism to calculate the correlation and dependency of trajectory features in multiple frequency subspaces, adaptively analysing trajectory feature changes in the time-frequency domain. Finally, we design an information aggregation context module. Based on the construction of the spatiotemporal relationship graph of agricultural machinery trajectory points, we combine it with a multi-filter graph convolution operator to capture the multi-scale features of trajectory points in different channels. To validate DCANet, we evaluated it on 3 datasets against 12 related methods. The results demonstrate that DCANet achieves superior performance, with accuracies of 89.03%, 90.24%, and 90.47%, and F1-scores of 71.81%, 84.62%, and 90.43%, respectively.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101774"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925563","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":"Integration DeepLabv3+ applied to RGB images and vegetation indices for nitrogen status in cereal–legume intercropping system","authors":"Z. Yao ,&nbsp;E. Denimal ,&nbsp;L. Dujourdy ,&nbsp;C. Gée","doi":"10.1016/j.atech.2025.101752","DOIUrl":"10.1016/j.atech.2025.101752","url":null,"abstract":"<div><div>This study presents a low-cost, non-invasive approach to monitor the nitrogen status of Triticale in a Triticale–Faba bean intercropping system, an agroecological strategy to avoid chemical nitrogen inputs, using consumer-grade smartphone RGB images and deep learning. Three smartphones (Samsung Galaxy A12, Xiaomi Redmi Note 4, and Redmi Note 11) were used to capture canopy images. A DeepLabV3+ model with a ResNet-50 backbone was trained to semantically segment Triticale pixels from mixed canopies. Training the model on patch-based image subsets, rather than full images, substantially enhanced segmentation accuracy (mIoU = 90.64%). The normalized Dark Green Color Index (nDGCI) derived from segmented images at the canopy scale, was evaluated as a proxy for nitrogen status against normalized SPAD (nSPAD) measurements, a tedious leaf scale method. Strong linear relationships were observed between nDGCI and nSPAD (pooled correlation across optical devices of R² ≈ 0.60 and optical device-specific ranging from R² = 0.69 to 0.87). Statistical analyses highlighted significant effects of cropping modality, phenological stage, and device on both indices, but the method reliably distinguished between nitrogen treatments. Device-specific calibration effectively corrected offsets, validating the feasibility of smartphone-based AI for detailed monitoring in intercropped systems. This approach offers a practical cost-effective alternative to conventional tools enabling precision agriculture in agroecological contexts.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101752"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925727","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":"CANBUS to drawbar load estimation: Mapping real-world tractor loads for mission profiling","authors":"Luca Colendi ,&nbsp;Manuel Tentarelli ,&nbsp;Massimiliano Varani ,&nbsp;Michele Mattetti","doi":"10.1016/j.atech.2026.101777","DOIUrl":"10.1016/j.atech.2026.101777","url":null,"abstract":"<div><div>With increasingly stringent requirements for greenhouse gas reduction, assessing current tractor drivetrains and developing cleaner alternatives are of growing importance. Designing such systems requires extensive field-load data, which remain difficult to obtain due to the absence of scalable, long-term measurement methods. This study presents a non-invasive methodology for evaluating tractor field loads using only controller area network (CANBUS) messages, universally available on modern tractors. A static mathematical model of a hydromechanical continuously variable transmission (IHMCVT) is developed, validated through experimental tests, and applied to map drawbar forces under real-world conditions. The model employs convergence-based calculations with multiple feedback loops and shallow neural networks to determine hydrostatic unit efficiencies. Validation against power-based measurements from road and field tests demonstrates accuracy within ±10%, suitable for practical applications. Applied to one year of operational data, the method reveals that tractors mainly operate at low speeds (&lt;10 km h⁻¹) under heavy soil tillage and transport conditions. Specific fuel consumption mapping further highlights inefficiencies due to engine–transmission interactions. Overall, the proposed CANBUS-based model provides a reliable, scalable, and low-complexity approach for real-world mission profiling and future drivetrain optimization.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101777"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925730","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":"Smart greenhouse climate control with real-time fault detection and energy-aware automation","authors":"Li Zeping ,&nbsp;Noramalina Abdullah ,&nbsp;Mohamad Khairi Ishak","doi":"10.1016/j.atech.2025.101707","DOIUrl":"10.1016/j.atech.2025.101707","url":null,"abstract":"<div><div>In response to increasing global climate variability and the environmental sensitivity of crop production, greenhouse cultivation has become an essential agricultural strategy. This study proposes a low-cost, modular intelligent temperature control system designed specifically for greenhouses. The system integrates ZigBee-based environmental sensing, ESP32-based edge computing, and the Home Assistant platform. Leveraging DHT11 temperature sensors, Tuya smart plugs, and low-code configuration via ESPHome, the architecture enables real-time climate monitoring and automated environmental regulation. A prototype greenhouse was constructed to experimentally evaluate system performance across five key dimensions: sensor placement, response time, energy efficiency, fault tolerance, and ZigBee communication range. Results show that sensors positioned at crop canopy height provided the most representative environmental data. The system maintained a total control latency under 90 s, balancing responsiveness with optimized energy use. A comparative analysis of two temperature control strategies, with ranges of 32 to 35 °C and 33 to 34 °C, respectively, revealed that the stricter range led to 2.2 times greater energy consumption, underscoring the inherent balance between temperature regulation precision and energy efficiency. ZigBee communication achieved over 140 m of line-of-sight range and demonstrated rapid self-healing capability under network disruption. The proposed approach supports the development of intelligent, data-driven environmental control systems for future smart farming applications and precision agriculture.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"13 ","pages":"Article 101707"},"PeriodicalIF":5.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790773","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}