Information Processing in Agriculture最新文献

{"title":"Plot level sugarcane yield estimation by machine learning on multispectral images: A case study of Bundaberg, Australia","authors":"Sharareh Akbarian ,&nbsp;Mostafa Rahimi Jamnani ,&nbsp;Chengyuan Xu ,&nbsp;Weijin Wang ,&nbsp;Samsung Lim","doi":"10.1016/j.inpa.2023.06.004","DOIUrl":"10.1016/j.inpa.2023.06.004","url":null,"abstract":"<div><div>Early crop yield prediction provides critical information for Precision Agriculture (PA) procedures, policymaking, and food security. The availability of Remote Sensing (RS) datasets and Machine Learning (ML) approaches improved the prediction of sugarcane crop yield on the local and global scales, but an additional effort on the plot scale prediction is required. Challenges for plot-level prediction include a high ratooning capacity of the sugarcane crop, the lack of high spatial resolution data during the critical growth stages, and the non-linear complexation of yield data. The principal objective of the study is to analyse the potential of a time series of high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery along with three advanced ML techniques, namely Random Forest Regression (RFR), Support Vector Regression (SVR), and Nonlinear Autoregressive Exogenous Artificial Neural Network (NARX ANN) as a solution to the plot-level sugarcane yield prediction. An experimental sugarcane field containing 48 plots was selected, and UAV imagery was collected during the three consecutive cropping seasons' early and middle crop growth stages. Each dataset per growth stage was analyzed separately to predict the sugarcane crop yield in an attempt to discover how early the prediction of pre-harvest yield can be achieved. The datasets of the first two cropping seasons were trained and tested using the three ML techniques, utilizing 10-fold cross-validation to avoid overfitting. The third cropping season dataset was then used to evaluate the reliability of the developed prediction models. The results show that the correlation of Vegetation Indices (VIs) with crop yield in the middle stage outperforms the early stage in all three ML models. Moreover, comparing these models indicates that the NARX ANN method outperformed the others in the middle stage with the highest correlation coefficient (R²) of 0.96 and the lowest Root Mean Square Error (RMSE) of 4.92 t/ha. It was followed by the SVR (R² = 0.52, RMSE of 14.85 t/ha), which performed similarly to the RFR method (R² = 0.48, RMSE = 11.20 t/ha). In conclusion, the best-suited model for predicting sugarcane yields during the middle growth stage is a NARX ANN model employing the Normalized Difference RedEdge (NDRE), which demonstrates the feasibility of the ML approaches to predict the plot level sugarcane yield at a specific period of growth as they are less sensitive to the inconsistency of data collection times.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 476-487"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48925313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of drying behavior and characteristics of potato slices in multi–stage convective cabinet dryer: Application of artificial neural network","authors":"Susama Chokphoemphun ,&nbsp;Somporn Hongkong ,&nbsp;Suriya Chokphoemphun","doi":"10.1016/j.inpa.2023.06.003","DOIUrl":"10.1016/j.inpa.2023.06.003","url":null,"abstract":"<div><div>The inconsistency in the quality of dried products at different coordinates within a conventional multi-stage convective cabinet dryer is a critical but often neglected problem. In this study, the drying behavior (moisture ratio) occurring in each drying tray layer and the drying characteristics (shrinkage or area ratio) occurring at different coordinates within a multi-stage convective cabinet dryer was assessed. Potato slices were used as raw materials in the drying process. Experiments were carried out by varying three different hot air velocities and two different drying temperatures. It was found that under the same hot air temperature and air velocity, the change in moisture content in each drying tray and the shrinkage in each coordinate of the potato slices were different. Artificial neural network model was used to predict the moisture ratio and the area ratio of the potato slices based on the experimental data. The moisture ratio obtained from the experiment was evaluated by comparing it with the drying model. The results showed a good confidence level with the coefficient of determination in the range of 0.962 7–0.993 3. The shrinkage analysis was based on the photographic data taken through image processing before usage as the output data for the predictive model. The predictive model was designed to have various architectures with different parameters; both hidden layer and hidden layer size, learning rate, training cycles, sampling type and split ratio. The best moisture ratio and area ratio model provided the coefficient of determination of 0.996 and 0.970, respectively.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 457-475"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49485571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mask R-CNN aided fruit surface temperature monitoring algorithm with edge compute enabled internet of things system for automated apple heat stress management","authors":"Basavaraj R. Amogi ,&nbsp;Rakesh Ranjan ,&nbsp;Lav R. Khot","doi":"10.1016/j.inpa.2023.12.001","DOIUrl":"10.1016/j.inpa.2023.12.001","url":null,"abstract":"<div><div>Our prior study focused on development of internet of things (IoT) and edge-compute enabled crop physiology sensing system (CPSS) for apple sunburn monitoring. Edge compute algorithm on CPSS estimated sunburn susceptibility as fruit surface temperature (FST) through pixel-by-pixel multiplication of captured thermal infrared images with segmented fruits binary mask. The segmentation was performed using color-based K means clustering approach. This limited CPSS applicability to monitor sunburn of red colored cultivars only and when fruits develop color, typically late growing season. This is a key research gap as recent weather patterns have shown that sunburn can occur during early growing season when fruits are green to yellow. Therefore, aim of this study was to develop and field evaluate cultivar and color independent mask region-convolution neural network (R-CNN) aided fruit segmentation model and edge compute compatible FST estimation algorithm. Season long field data were collected in 2021 using eight CPSS nodes (three in cv. WA38 [Cosmic crisp] and five in cv. Honeycrisp). Collected data were used to develop and validate mask R-CNN based fruit segmentation model. Developed mask R-CNN based model was able to segment fruits of two apple cultivars and of varying colors with 91.4 % average precision. In orchard evaluations (2022 season), the resulting algorithm ported on CPSS was able to accurately segment (dice similarity coefficient = 0.89) and estimate apple FST with &lt; 0.5 °C error compared to ground truth data. With compute time of about 37 s, data processing time was reduced by 22 % over previous algorithm. High ambient temperature (&gt;35 °C) on a warmer day resulted in multiple throttling errors caused by excessive CPU temperature; however, the CPSS performance was uncompromised in FST estimation. Ambient air temperature did not affect RAM utilization and CPU clock frequency. Overall, developed FST algorithm can potentially be used as input to actuate water-based cooling system.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 603-611"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138613036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new greenhouse energy model for predicting the year-round interior microclimate of a commercial greenhouse in Ontario, Canada","authors":"Alex Nauta,&nbsp;Jingjing Han,&nbsp;Syeda Humaira Tasnim,&nbsp;William David Lubitz","doi":"10.1016/j.inpa.2023.06.002","DOIUrl":"10.1016/j.inpa.2023.06.002","url":null,"abstract":"<div><div>Modelling the energy use and microclimate of a greenhouse can be a valuable tool for commercial growers, making it possible to predict the impact of making changes to greenhouse systems and operation. This allows energy saving scenarios to be identified and can reduce energy use costs. In this study, a lumped capacitance thermal model is developed to simulate the greenhouse interior microclimate based on exterior conditions and operating settings. The current study incorporated many aspects of a complex commercial greenhouse not commonly seen in literature, such as evaporative cooling pads, dehumidification technology, gas burners, energy curtains, supplementary heating and lighting, and forced ventilation. The model was successfully validated at multiple greenhouse sections of a commercial greenhouse during spring, summer, and fall conditions in the southern Ontario climate. Data was collected from the greenhouse from March to November of 2019 at 15-minute intervals. The measured interior temperature and relative humidity data were used to evaluate the accuracy of the model simulations, while other measurements, such as solar radiation, were used as model inputs. The study greenhouse was unique, as potted rose crops were cycled between sections during the growth stage. This made variation in plant properties relatively small during the different seasons. Detailed information on the model methodology was included to improve reader’s understanding. Overall, the model accuracy is comparable or even better when compared to similar models in the literature, proving it is versatile and can be used as a design tool moving forward. In the future, the current model will be used to conduct comparative analyses of a range of different energy-use reduction technologies and operating procedures (including year-round production) to quantify the most economically and practically feasible options specifically for Ontario greenhouse growers.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 438-456"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43514932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review on beef cattle supplementation technologies","authors":"Guilherme Defalque ,&nbsp;Ricardo Santos ,&nbsp;Marcio Pache ,&nbsp;Cristiane Defalque","doi":"10.1016/j.inpa.2023.10.003","DOIUrl":"10.1016/j.inpa.2023.10.003","url":null,"abstract":"<div><div>The increase in the worldwide population reflects the expansion of beef cattle production and exportation. Although pasture is the world’s primary feed source of cattle food, failures in pasture management can endanger the productivity of beef cattle. An option for reducing the issues brought on by a shortage of nutritional resources and maintaining the fodder pasture is to perform the supplementation process on the livestock, even being one of the most costly activities in animal management. To decrease expenses and the need for labor to supplement the herd and improve animal performance, many parameters directly associated with supplementation must be monitored, such as environmental climate, soil and pasture characteristics, animal welfare, weight, and health. With so many parameters that impacts the decision on the quality and quantity of supplement to be supplied to the herd, sensors, remote sensing, and agricultural machinery are essential. The joint usage of these technologies in the supplementation process is complex, and there is a gap in decision-making systems for dynamic supplementation. Therefore, this work aims to carry out a comprehensive literature review that characterizes the main technologies related to the bovine supplementation process, mapping the main processes that involve the use of technological tools in the most diverse application domains. Finally, we propose a new Internet of Things architecture focused on the cattle supplementation process that combines technologies to compose a dynamic supplementation decision-making system capable of estimating the quantity and quality of the supplement that the herd needs in the presence of changes in the environment, pasture, and animals’ conditions parameters to reach production targets.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 559-572"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated tree ring detection of common Indiana hardwood species through deep learning: Introducing a new dataset of annotated images","authors":"Fanyou Wu ,&nbsp;Yunmei Huang ,&nbsp;Bedrich Benes ,&nbsp;Charles C. Warner ,&nbsp;Rado Gazo","doi":"10.1016/j.inpa.2023.10.002","DOIUrl":"10.1016/j.inpa.2023.10.002","url":null,"abstract":"<div><div>Tree-ring dating enables gathering necessary knowledge about trees, and it is essential in many areas, including forest management and the timber industry. Tree-ring dating can be conducted on either wood’s clean cross-sections or tree trunks’ rough end cross-sections. However, the measurement process is still time-consuming and frequently requires experts who use special devices, such as stereoscopes. Modern approaches based on image processing using deep learning have been successfully applied in many areas, and they can succeed in recognizing tree rings. While supervised deep learning-based methods often produce excellent results, they also depend on extensive datasets of tediously annotated data. To our knowledge, there are only a few publicly available ring image datasets with annotations. We introduce a new carefully captured dataset of images of hardwood species automatically annotated for tree ring detection. We capture each wood cookie twice, once in the rough form, similar to industrial settings, and then after careful cleaning, that reveals all growth rings. We carefully overlap the images and use them for an automatic ring annotation in the rough data. We then use the Feature Pyramid Network with Resnet encoder that obtains an overall pixel-level area under the curve score of 85.72% and ring level <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> score of 0.7348. The data and code are available at <span><span>https://github.com/wufanyou/growth-ring-detection</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 552-558"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135455319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recognition and statistical method of cows rumination and eating behaviors based on Tensorflow.js","authors":"Yu Zhang,&nbsp;Xiangting Li,&nbsp;Zhiqing Yang,&nbsp;Shaopeng Hu,&nbsp;Xiao Fu,&nbsp;Weizheng Shen","doi":"10.1016/j.inpa.2023.11.002","DOIUrl":"10.1016/j.inpa.2023.11.002","url":null,"abstract":"<div><div>Information about dairy cow ruminating is closely associated with the health status of dairy cows. Therefore, it is of great significance to recognize and make statistics of dairy cows’ ruminating and feeding behavior. Concerning conventional recognition methods which are dependent on contact type devices, they have some defects of poor instantaneity and strong stress responses. As for recognition based on machine vision, it needs to transmit masses of data and raises high requirements for the cloud server and network performance. According to principles of edge computing, the model is deployed via Tensorflow.js in an edge device in the present study, constructing a recognition and statistical system for ruminating and feeding behavior of dairy cows. Through the application programming interface (API) of the browser, an edge device is able to invoke a camera and acquire dairy cow images. Then, the images can be inputted in the SSD MobileNet V2 model, which is followed by inference based on browser hashrate. Moreover, the edge device merely uploads recognition results to the cloud server for statistics, which features high instantaneity and compatibility. In terms of recognizing ruminating and feeding behavior of dairy cows, the proposed system has a precision ratio of 96.50%, a recall rate of 91.77%, an F1-score of 94.08%, specificity of 91.36%, and accuracy of 91.66%. This suggests that the proposed method is effective in recognizing dairy cow behavior.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 581-589"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135514893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid genetic slime mould algorithm for parameter optimization of field-road trajectory segmentation models","authors":"Jiawen Pan,&nbsp;Caicong Wu,&nbsp;Weixin Zhai","doi":"10.1016/j.inpa.2023.11.003","DOIUrl":"10.1016/j.inpa.2023.11.003","url":null,"abstract":"<div><div>Field-road trajectory segmentation (FRTS) is a critical step in the processing of agricultural machinery trajectory data. This study presents a generalized optimization framework based on metaheuristic algorithms (MAs) to increase the accuracy of the field-road trajectory segmentation model. The MA optimization process is used in this framework to precisely and quickly identify the parameters of the FRTS model. It is difficult to solve the parameter optimization problem with basic metaheuristic algorithms without falling into local optima due to their insufficient performance. This study therefore combines a genetic algorithm (GA) with a slime mould algorithm (SMA) to propose a novel enhanced hybrid algorithm (GASMA); the algorithm has superior global search capability due to the implicit parallelism of the GA, and the oscillation concentration mechanism of the SMA is used to enhance the algorithm's local search capability. To maintain the balance between the two capacities, a nonlinear parameter management technique is developed that adaptively modifies the algorithm's computational process based on the fitness distribution deviation of the population. Experiments were conducted on real agricultural trajectory datasets with various sample frequencies, and the proposed algorithm was compared with existing methods to validate its efficiency. According to the experimental data, the optimized model produced better results. The proposed approach provides an automatic and accurate method for determining the optimal parameter configurations of FRTS model instances, where the parameter optimization solution is not confined to a single specified procedure and can be addressed by a variety of metaheuristic algorithms.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 590-602"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139299152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement Learning system to capture value from Brazilian post-harvest offers","authors":"Fernando Henrique Lermen ,&nbsp;Vera Lúcia Milani Martins ,&nbsp;Marcia Elisa Echeveste ,&nbsp;Filipe Ribeiro ,&nbsp;Carla Beatriz da Luz Peralta ,&nbsp;José Luis Duarte Ribeiro","doi":"10.1016/j.inpa.2023.08.006","DOIUrl":"10.1016/j.inpa.2023.08.006","url":null,"abstract":"<div><div>This study assesses the value capture of a result-oriented Product-Service System offer that constitutes a post-harvest solution. Applying the reinforcement learning reward system and general linear models, we identified the Brazilian farmer's propensities to choose different products and services from the proposed system. Reinforcement learning enables one to understand the choice process by rewarding the attributes selected and applying penalties to those not chosen. Regarding product options, farmers' most valued attributes were extended capacity, fixed installation, automatic dryer, and CO₂ emission control, considering the investigated system. Regarding service options, the farmers opted for maintenance plans, performance reports, no photovoltaic energy, and purchase over the rental modality. These results assist managers through a reward learning system that constantly updates the value assigned by farmers to product and service attributes. They allow real-time visualization of changes in farmers' preferences regarding the product-service system configurations.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 499-511"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42681215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion of RetinaFace and improved FaceNet for individual cow identification in natural scenes","authors":"Lingling Yang,&nbsp;Xingshi Xu,&nbsp;Jizheng Zhao,&nbsp;Huaibo Song","doi":"10.1016/j.inpa.2023.09.001","DOIUrl":"10.1016/j.inpa.2023.09.001","url":null,"abstract":"<div><div>Cows’ posture change is the fatal influencing factor for accurate identification of individual cows. To achieve non-contact, high-precision detection and identification of individual cows in farm environment, a cow individual identification method by the fusion of RetinaFace and improved FaceNet was proposed. MobileNet-enhanced RetinaFace was applied to ameliorate the impact of output channel quantity and convolution kernel dynamics using depthwise convolution combined with pointwise convolution. Regression predictions of bovine facial features and keypoints were generated under varying distances, scales and sizes. FaceNet's core feature network was enhanced through MobileNet integration, and the loss function was jointly optimized with Cross Entropy Loss and Triplet Loss to achieve a quicker and more stable convergence curve. The distances between the generated embedding vectors of cow facial features were corresponding to the similarity between cow faces, enabling accurate matching. RetinaFace exhibited detection false negative rates of 2.67%, 0.66%, 2.67%, and 3.33% under conditions of occlusion, no occlusion, low light, and bright light for cow facial detection. For cow facial pattern detection, the false negative rates for black and white patterns, pure black and pure white were 1.33%, 6.00% and 8.00%, respectively. Regarding cow facial posture changes, the false negative rates for face upward, bowing down, profile, and normal posture were 1.33%, 1.33%, 4.00% and 0.66%, respectively. Improved FaceNet model achieved an accuray of 99.50% on training set and 83.60% on test set. In comparison to YOLOX, the recognition model presented in this research demonstrated increased accuracy in cow facial detection under occlusion, no occlusion and strong lighting conditions by 2.67%, 0.40%, and 0.40%, respectively. Moreover, the accuracy for patterns with pure black and pure white tones surpassed that of YOLOX by 1.06% and 5.71%, correspondingly. Additionally, the accuracy rates for face upward, bowing down, profile and normal posture were higher than YOLOX by 2.00%, 3.34%, 2.66% and 0.40%, respectively. The proposed model demonstrates the proficiency in accurately identifying individual cows in natural scenes.</div></div>","PeriodicalId":53443,"journal":{"name":"Information Processing in Agriculture","volume":"11 4","pages":"Pages 512-523"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42302982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}