Smart agricultural technology最新文献

{"title":"Repelling of stored pest (rice weevil) through near-far ultrasound","authors":"Md. Abdul Awal ,&nbsp;Israth Jahan Zune ,&nbsp;Pronab Kumar Paul Partha","doi":"10.1016/j.atech.2025.100961","DOIUrl":"10.1016/j.atech.2025.100961","url":null,"abstract":"<div><div>Rice is the most important stored product in the agricultural landscape of Bangladesh. The preservation of rice faces a significant loss due to the widespread presence of stored pests. Rice weevil (Sitophilus oryzae) is the common and destructive pest in stored rice. Numerous approaches exist for repelling stored pests, involving chemical solutions, biological control, and disinfestation through fumigation. However, the practical viability of these methods has been compromised due to their associated costs and environmental implications. As a result, there is an emergent need for exploring alternative and environmentally friendly pest management strategies to control stored pests in a sustainable manner. This study explored the efficacy of near-far ultrasound as a repellent technique for rice weevils. The near-far ultrasound system was constructed in combination of the microcontroller, ultrasound sensor, temperature and humidity sensor, and C++ language was used to run the system. A multi-web-cam system was deployed to monitor weevils’ movements in radiation and non-radiation chambers. A total of 100 live weevils were used for each experiment. The developed frequency range of 33–48 KHz was automatically sent to the transducer and the radiation on weevils was observed through their movement changed during the experimental time. The results showed that 29 ± 0.812 %, 54 ± 0.927 % and 79 ± 0.922 % were effective after 24, 48 and 72 h of experimentation. Hence, the developed ultrasonic weevil repellent system can be a practical and sustainable solution for repelling weevils, which mitigates storage losses caused by weevil infestations.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100961"},"PeriodicalIF":6.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868967","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":"Leaf area index-based phenotypic assessment of sweet potato varieties using UAV multispectral imagery and a hybrid retrieval approach","authors":"Philemon Tsele ,&nbsp;Abel Ramoelo ,&nbsp;Lucy Moleleki ,&nbsp;Sunette Laurie ,&nbsp;Whelma Mphela ,&nbsp;Natasha Tshuma","doi":"10.1016/j.atech.2025.100960","DOIUrl":"10.1016/j.atech.2025.100960","url":null,"abstract":"<div><div>Phenotyping based on the estimation of plant traits such as the leaf area index (LAI) could aid the identification and monitoring of the sweet potato health, growth status and gross primary productivity. Integrating radiative transfer models (RTMs), active learning algorithms and non-parametric regression methods using unmanned aerial vehicle (UAV) multispectral imagery have the potential for accurately estimating LAI across multiple crop varieties at varying growth stages. This study tested the boosted regression trees (BRT) and kernel ridge regression (KRR) for inversion of the PROSAIL RTM to retrieve LAI across 20 sweet potato varieties during peak growth stage. Furthermore, the study attempted to constrain the inversion process by using active learning (AL) techniques which ensured the selection of informative samples from a pool of RTM simulations. Results show that the most accurate LAI retrieval over the heterogeneous sweet potato canopy was achieved by integrating smaller PROSAIL simulations with the random sampling AL and KRR methods. The LAI retrieval accuracy had a coefficient of determination (R²) of 0.52, root mean squared error (RMSE) of 0.88 m².m^-2 and relative RMSE of 12.23 %. However, the BRT performance in-comparison to KRR, captured more spatial variability of observed LAI with a better prediction accuracy across the 20 sweet potato varieties. The hybrid approach developed in this study, show potential for accurate phenotyping of LAI dynamics across multiple sweet potato varieties during a matured growth stage. These findings have significant implications for sweet potato breeding programmes that are critical for developing new cultivars in South Africa.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100960"},"PeriodicalIF":6.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868970","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":"Community-based business models for agricultural and forestry data ecosystems: A systematic literature review","authors":"Samin Ghalandarzadeh ,&nbsp;Arianit Kurti ,&nbsp;Cecilia Unell ,&nbsp;Adam Hallborg ,&nbsp;Zenun Kastrati ,&nbsp;Tinh Sjökvist","doi":"10.1016/j.atech.2025.100958","DOIUrl":"10.1016/j.atech.2025.100958","url":null,"abstract":"<div><div>Data-driven solutions are becoming essential to modern business models, changing traditional business practices and complex value chains in multi-stakeholder and community-based sectors such as agriculture and forestry. Nevertheless, there is a lack of consolidated knowledge about the benefits and challenges that data-driven community-based business models may present in these domains. This study conducts a systematic literature review of scientific publications to identify the benefits and barriers that community-based business models for agriculture and forestry data ecosystems present. The articles included are in English and peer-reviewed and were published between 2014 and 2024. The search was conducted in Scopus, Web of Science, and IEEE Xplore, and the query resulted in 387 studies. This review has followed the PRISMA methodology, and the final number of reviewed papers was 51. Ultimately, it is found that the benefits outweigh the barriers in terms of their repetition across the literature. Significant benefits identified are interconnectedness and interactivity, resource availability, and multidirectional knowledge transfer, while the high cost of implementation and the complexity of integration and implementation of data-driven community-based business models are among the major barriers. The findings from this work can bridge the existing gap of attention to data-driven community-based business models in agriculture and forestry, help with future research work, and act as guidelines for implementing such business models.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100958"},"PeriodicalIF":6.3,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868968","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 rice disease and insect-pest detection through augmented deep learning with transfer learning techniques","authors":"Amit Bijlwan ,&nbsp;Rajeev Ranjan ,&nbsp;Shweta Pokhariyal ,&nbsp;Ajit Govind ,&nbsp;Manendra Singh ,&nbsp;Krishna Pratap Singh ,&nbsp;Raj Kumar Singh ,&nbsp;Ravindra Kumar Singh Rajput ,&nbsp;Rajeev Kumar Srivastava","doi":"10.1016/j.atech.2025.100954","DOIUrl":"10.1016/j.atech.2025.100954","url":null,"abstract":"<div><div>The timely and accurate identification and prediction of crop diseases and insect pests are essential for effective crop management. This research provides a thorough evaluation of various deep learning (DL) models focused on the classification and identification of rice diseases, as well as rice insect pests. A detailed dataset for recognizing and classifying rice diseases and insect pests was gathered from both experimental and farmer’s fields in and around Pantnagar, Udham Singh Nagar district, Uttarakhand. The dataset, collected over the two kharif seasons of 2022 and 2023, encompasses a wide range of pathological and entomological specimens. The dataset includes images of various diseases such as brown spot, sheath blight, bacterial leaf blight (BLB), and false smut, in addition to samples of healthy leaves. The pest specimens identified in rice include rice hispa, stem borer (including eggs), rice gundhi bug, demsel fly, leaf folder larvae, and Pyrilla perpusilla. Among the models tested for rice disease classification, the EfficientNetB0 model demonstrated the highest performance, reaching an impressive test accuracy of 98.07%, with exceptional precision (0.9953), recall (0.9860), and F1 scores (0.9906) for Sheath Blight. Meanwhile, EfficientNetB7 also performed robustly with a test accuracy of 96.59%. In the classification of rice insect pests, EfficientNetB0 outperformed others with a test accuracy of 99.45% and minimal test loss (0.0278), achieving perfect precision, recall, and F1 scores for classes like Gundhi Bug and Stem Borer (eggs). EfficientNetB7 followed closely, attaining a test accuracy of 99.72%, with minor variations in recall for certain classes.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100954"},"PeriodicalIF":6.3,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873815","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":"The assessment of soil variables relative importance for cereal yield prediction under rainfed cropping system in Morocco","authors":"Oumnia Ennaji ,&nbsp;Abdellah Hamma ,&nbsp;Leonardus Vergütz ,&nbsp;Achraf El Allali","doi":"10.1016/j.atech.2025.100950","DOIUrl":"10.1016/j.atech.2025.100950","url":null,"abstract":"<div><div>In this study, Explainable Artificial Intelligence (XAI) techniques were applied to identify the most important factors influencing crop yield prediction, with a focus on strategies for sustainable agriculture. Using permutation importance and residual plot analysis, the results showed that nitrogen (N) content, Bandera variety and potassium oxide (<span><math><msub><mrow><mtext>K</mtext></mrow><mrow><mn>2</mn></mrow></msub><mtext>O</mtext></math></span>) are the most important traits influencing yield prediction. Extreme Gradient Boosting (XGB) was used to predict yield using a large Moroccan national cereal dataset spanning 3 seasons. Residual Plots Analysis, Partial Dependent Plots (PDP), Permutation Importance (PI) and SHapley Additive ExPlanations (SHap) were used to select the features that influence yield prediction. The results indicate that optimizing soil nitrogen and potassium oxide levels together with strategic selection of crop varieties can significantly increase productivity. Residual analysis of the eXtreme Gradient Boosting (XGB) model confirmed its high predictive accuracy. This study underlines the value of XAI methods in improving the interpretability of predictive models. The insights gained can contribute to better soil management and informed crop selection, ultimately reducing yield losses under environmental stress. By increasing the resilience of agricultural systems, we aim to contribute to sustainable and data-driven farming practices and, in particular, address some of Morocco's unique agricultural challenges.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100950"},"PeriodicalIF":6.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843319","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":"Prediction of black soldier fly larval sex and morphological traits using computer vision and deep learning","authors":"Sarah Nawoya ,&nbsp;Quentin Geissmann ,&nbsp;Henrik Karstoft ,&nbsp;Kim Bjerge ,&nbsp;Roseline Akol ,&nbsp;Andrew Katumba ,&nbsp;Cosmas Mwikirize ,&nbsp;Grum Gebreyesus","doi":"10.1016/j.atech.2025.100953","DOIUrl":"10.1016/j.atech.2025.100953","url":null,"abstract":"<div><div>The growing interest in insect farming as a sustainable protein alternative has given rise to the commercial production of key species like the Black Soldier Fly (BSF), primarily for use in livestock, fish, and pet nutrition. Despite the heightened interest in BSF production, there is a need for increased efficiency, particularly in the context of large-scale measurement of various traits for selective breeding as well as management optimization. The unique insect production systems, coupled with the challenges posed by their small size, fragility, and metamorphic life cycle underscores the necessity for innovative approaches to streamline production.</div><div>This study explores the potential of computer vision (CV) in predicting the larval sex and morphological traits of BSF, offering a non-invasive, rapid, and automated method for trait measurement. The study explores algorithms utilizing You-Only-Look-Once (YOLOv8) in detection and segmentation, ResNet for feature extraction and classification, and regression analysis mechanisms. We assess the ability of our models to predict larval weight from images through morphometric weight prediction and CNN-regression approaches.</div><div>A notable contribution of this study is the pioneering effort to classify BSF larval sex using CV and deep learning (DL). In the analysis of larval weight prediction, a coefficient determination (R²) of up to 0.80 between measured and predicted weight was achieved using the morphometric weight prediction approach, along with an R² of 0.71 through the CNN-regression approach. Additionally, the sex prediction module demonstrated an F1 score of 0.75 and a prediction accuracy of 74 %. These results underscore the feasibility of leveraging CV techniques for predicting the sex and body traits of BSF larvae, representing a significant advancement toward the automation of selective breeding in the context of insect farming.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100953"},"PeriodicalIF":6.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850531","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":"Exploring bare soil digital mapping: identifying alternative variables to replace ECa via remote sensing, a case study on two Italian fields at different latitude","authors":"Matteo Petito ,&nbsp;Emanuele Barca ,&nbsp;Antonio Berti ,&nbsp;Silvia Cantalamessa ,&nbsp;Giancarlo Pagnani ,&nbsp;Michele Pisante","doi":"10.1016/j.atech.2025.100955","DOIUrl":"10.1016/j.atech.2025.100955","url":null,"abstract":"<div><div>Site-specific management in agriculture, which accounts for variability within a field, is a cornerstone of sustainable agronomic practices. However, despite the availability of numerous methods to measure spatial variability, their limitations hinder large-scale adoption, posing challenges to the broader implementation of precision agriculture. This study aims to identify spectral indices derived from bare-soil analysis as potential substitutes for electrical conductivity (ECa) in mapping spatial variability. The approach aligns with the need for cost-effective, scalable, and less labor-intensive solutions to manage field variability. Using multi-temporal bare-soil imagery spanning five years across two fields under intermittent cultivation in Italy, we applied principal component analysis to evaluate correlations between spectral indices and ECa. Both fields demonstrated strong correlations between ECa and the first principal component (PC1). Key variables identified as highly correlated with ECa included the Brightness Index (0.66), Near-Infrared (0.53), and Red reflectance (0.58). The percentage variance explained by PC1 was 75.4 % for Field 1 and 79.0 % for Field 2. Finally, PC1 is correlated with ECa in the two areas in the measure of 0.73 and 0.53, respectively. This work addresses the problem of substituting ECa with covariates derived from bare-soil analysis from a purely statistical perspective as a first necessary step aiming at identifying the most promising covariates. A subsequent study will address this issue from a pedological standpoint. These findings highlight the potential of remote sensing data and spectral indices from multi-temporal imagery to replace direct ECa measurements, enabling rapid and accurate mapping of spatial variability in annual croplands.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100955"},"PeriodicalIF":6.3,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based detection of green-ripe pineapples via bract wilting rate analysis","authors":"Guo-Fong Hong ,&nbsp;Sumesh Nair ,&nbsp;Chun-Yu Lin ,&nbsp;Ching-Shan Kuan ,&nbsp;Shean-Jen Chen","doi":"10.1016/j.atech.2025.100949","DOIUrl":"10.1016/j.atech.2025.100949","url":null,"abstract":"<div><div>Green-ripe pineapples are ideal for long-term transportation and storage during summer. However, accurately identifying them during <em>in-situ</em> harvesting remains a challenge for farmers. To address this issue, this study proposes a deep learning-based YOLO<img>NAS-L algorithm to detect green-ripe pineapples by analyzing the wilting rate of floral bracts at the fruit's base. An unmanned tracked vehicle equipped with an Intel D405 depth camera was used to traverse pineapple fields, capturing images from a distance of 300–400 mm. Each image covered approximately 20 floral bracts, with a detection resolution of around 30 × 30 pixels. The camera also provided three-dimensional coordinates of the pineapples to support automated harvesting. To mitigate ambient light variations, a white LED lighting system (24V/5A) was implemented for illumination enhancement. Experimental results indicate that analyzing floral bract wilting improves green-ripe pineapple recognition accuracy by 13.6 %, reaching 95.4 %, compared to solely identifying the pineapple's base. These findings demonstrate that deep learning-based floral bract wilting analysis significantly enhances recognition accuracy and provides robust support for automated harvesting.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100949"},"PeriodicalIF":6.3,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828410","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":"Early and accurate nutrient deficiency detection in hydroponic crops using ensemble machine learning and hyperspectral imaging","authors":"Nagarajan S․ ,&nbsp;Maria Merin Antony ,&nbsp;Murukeshan Vadakke Matham","doi":"10.1016/j.atech.2025.100952","DOIUrl":"10.1016/j.atech.2025.100952","url":null,"abstract":"<div><div>Vertical indoor hydroponic farms are growing as a technological solution fostering agriculture productivity to address the ever increasing food demands in sustainable cities. These farms provide extensive control over the growing conditions to ensure all-weather cultivation of diverse crops within the available limited space. However, to assure the quality of hydroponic crops, continuous close-range crop monitoring and early detection of deficiencies are essential. Sensitive techniques such as hyperspectral imaging combined with ensemble based machine learning techniques have proven to provide improved reliable results. However, despite their potential, the application of these methods for early-stage nutrient deficiency detection in crops remains relatively underexplored. In this context, this research presents and proposes different machine learning-based approaches that utilizes ensemble techniques such as Random Forest (RF), Bagging or Bootstrap Aggregating, Adaboost or Adaptive Boosting, and eXtreme Gradient Boosting (XGB) classifiers for early detection of nutrient deficiencies in hydroponic crops. In the proposed approach, the features extracted from hyperspectral datacubes are trained to create machine learning models. Among the investigated models, the XGB classifier demonstrated the fastest computational time and test accuracy of 18.07 s and 99.6 %, respectively. This research also proposes a novel computer vision (CV) based approach to improve the tedious manual data labelling process involved in HSI dataset creation. Envisioned as an invaluable tool, the proposed non-invasive imaging system could detect as early as 3 days after stress induction and revolutionize the automated monitoring of indoor hydroponic farms with enhanced accuracy for a sustainable future.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100952"},"PeriodicalIF":6.3,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839454","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":"Effects of viewing angle and field of view on detection, tracking, and counting of pine seedlings towards automated forest nursery inventory","authors":"Ashish Reddy Mulaka ,&nbsp;Rafael Bidese ,&nbsp;Yin Bao","doi":"10.1016/j.atech.2025.100951","DOIUrl":"10.1016/j.atech.2025.100951","url":null,"abstract":"<div><div>The current inventory practice in bareroot forest nurseries relies on manually counting tree seedlings in randomly sampled plots to estimate the stock for each seed lot. This method is labor-intensive, time-consuming, and susceptible to human error. Recent advances in deep learning-based object detection and efficient tracking algorithms have enabled automated object counting in video data across various domains, including crop seedling counting in agriculture. This study investigates the effects of viewing angle (VA) and field of view (FoV) on detection, tracking, and counting early-stage pine seedlings in nadir-view videos using a detect-and-track approach. We evaluated the performance of YOLOv8–10 models in conjunction with three multi-object tracking (MOT) algorithms (SORT, ByteTrack, and BoT-SORT) on a custom MOT dataset comprising an average of 153 seedlings per frame and totaling 166,440 seedlings. Detection results and statistical tests showed that increasing horizontal VA reduces the intersection over union (IoU) of seedling detections, primarily due to the perspective differences introduced by oblique viewing angles. MOT evaluations further demonstrated that BoT-SORT consistently delivered high counting accuracy when the vertical FoV encompassed at least the entire seedling. In contrast, ByteTrack and SORT exhibited significantly lower performance, producing reasonable counting accuracy only when the vertical FoV was sufficiently large. The superior performance of BoT-SORT is attributed to its camera motion compensation, which effectively reduces identity switches and tracking failures in scenes involving stationary yet overlapping seedlings. Notably, BoT-SORT achieved 100 % counting accuracy under a 20° horizontal VA across YOLO model sizes. Furthermore, larger YOLO models showed greater robustness to increases in horizontal VA. These findings provide valuable guidance for optimizing camera configurations and model selection towards the development of a real-time inventory systems for precision forest nursery management.</div></div>","PeriodicalId":74813,"journal":{"name":"Smart agricultural technology","volume":"11 ","pages":"Article 100951"},"PeriodicalIF":6.3,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839453","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}