Computers and Electronics in Agriculture最新文献

{"title":"Calibration of mass-spring-damper equivalent systems for real time assessment of the dynamics of trees","authors":"Ernesto Grande ,&nbsp;Raffaella Franceschini","doi":"10.1016/j.compag.2024.109610","DOIUrl":"10.1016/j.compag.2024.109610","url":null,"abstract":"<div><div>In-situ tests and numerical models represent valuable tools for deriving the main dynamic characteristics of trees and for studying their response to dynamic actions. Regarding the numerical models, a key aspect is their calibration. Most procedures available in the literature generally suggest the use of a significant number of instruments (accelerometers placed on both the trunk and branches), which results in high costs and is time-consuming. The aim of this paper is to propose a two-phase approach to calibrate multiple mass-spring-damper systems for studying the dynamics of trees. The proposal aims to support the monitoring and stability assessment of trees through an efficient procedure that combines techniques and methods derived from the field of structural dynamics. Some of these techniques are already used for trees, while others are newly applied in this context. In particular, the experimental data deduced from pull-release tests performed using a single accelerometer placed only on the trunk are assumed as the input data for the approach. The approach is presented in the first part of the paper. In the second part, the approach is implemented in the computer code Matlab to validate it with reference to both numerical models and real tree cases. Finally, a user-friendly graphical application of the approach is developed to make it a practical and expedient tool for researchers and practitioners, allowing real-time evaluation of the dynamics of trees, conducted simultaneously with in-situ tests.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109610"},"PeriodicalIF":7.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661766","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":"Non-destructive detection of wheat moisture content with frequency modulated continuous wave system under L and S bands","authors":"Xiaofei Kuang,&nbsp;Zhe Zhu,&nbsp;Jiao Guo,&nbsp;Shiyu Xiang","doi":"10.1016/j.compag.2024.109644","DOIUrl":"10.1016/j.compag.2024.109644","url":null,"abstract":"<div><div>Wheat moisture content is a critical indicator for evaluating quality. The microwave free space measurement method can achieve nondestructive and efficient measurement of wheat moisture. Regarding microwave detection technology for wheat moisture content, further validation is needed for establishing a prediction model using multi-frequency and full-frequency data within a specific band. Due to the excellent penetration capability of microwaves in the L and S bands, this study explores the potential of utilizing multi-frequency and full-frequency signals in these bands to develop a prediction system for wheat water content. The paper analyzes the relationship between different microwave frequencies, temperatures, moisture contents, and bulk densities on dielectric properties. Temperature, bulk density, and dielectric properties serve as characteristic parameters for the regression model, and a moisture prediction model incorporating single frequency, multi-frequency, and full-frequency data is established. The moisture content detection model integrates three regression methods: Partial Least Squares (PLS), Support Vector Regression (SVR), and Extreme Learning Machine (ELM). Results show that among the nine different prediction models, the SVR model under full-frequency conditions performs the best. The correlation coefficient, root mean square error, and residual prediction bias for moisture prediction on the validation set are 0.9838, 0.3511%, and 6.3245, respectively. To enable online detection of wheat moisture content, a low-cost frequency modulated continuous wave (FMCW) detection system was designed based on the optimal prediction model. Experiments have confirmed that within the moisture content range of 11.35% to 17.79%, the average determination coefficient between the moisture content obtained through drying methods and the measurement results from the FMCW system can reach 0.9493. These endeavors have the potential to provide reliable and cost-effective solutions for precision agriculture applications.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109644"},"PeriodicalIF":7.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661776","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":"Tomato maturity detection based on bioelectrical impedance spectroscopy","authors":"Zhang Yongnian ,&nbsp;Chen Yinhe ,&nbsp;Bao Yihua ,&nbsp;Wang Xiaochan ,&nbsp;Xian Jieyu","doi":"10.1016/j.compag.2024.109553","DOIUrl":"10.1016/j.compag.2024.109553","url":null,"abstract":"<div><div>This paper proposes a method for detecting tomato maturity to address issues related to post-harvest storage and transportation. The method utilizes bioimpedance spectroscopy to study tomato maturity, construct the Double-R-Cole equivalent circuit model, and obtain electrical parameters through fitting the Levenberg-Marquardt optimization algorithm. We analyze the change rule of electrical parameters in different ripening periods, use Fisher’s discriminant to reduce the dimensionality of features such as biological variables, fitted electrical parameters, and storage days of tomato, and combine the advantages of support vector machine and random forest to classify the input features. The classification algorithm utilizes the gorilla troop optimization algorithm to address issues with traditional iterative algorithms, such as difficulty assigning initial values and susceptibility to local optima. The study finds that: the Levenberg-Marquardt algorithm fitted an R^2 mean value of 0.997 and the significance level of <em>p</em> &lt; 0.001 between the two constant-phase components of the fitted electrical parameters and the number of storage days proved that the established Double-R-Cole model could effectively characterize the postharvest situation of tomato; the Fisher’s discriminant based SVM-RF-GTO’s maturity classification algorithm achieves 97.26 % effectiveness in tomato maturity classification. This research provides valuable insights for tomato postharvest storage and transportation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109553"},"PeriodicalIF":7.7,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661779","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":"Developments in deep learning approaches for apple leaf Alternaria disease identification: A review","authors":"Mansoor Ahmad Kirmani,&nbsp;Yasir Afaq","doi":"10.1016/j.compag.2024.109593","DOIUrl":"10.1016/j.compag.2024.109593","url":null,"abstract":"<div><div>Apple tree leaf diseases (ATLDs) can be accurately identified and addressed early to prevent the diseases from spreading, minimize the need for chemical pesticides and fertilizers, increase apple quality and production, and preserve the healthy growth of apple varieties. To overcome such challenges, different Deep Learning (DL) approaches have been developed to early detect apple leaf diseases. In this paper, the data from 2010 to 2024 has been taken for analysis, and it has been observed that many of the researchers have utilized different types of datasets for disease detection. Moreover, Deep Learning (DL) and Machine Learning (ML) have been mostly utilized for the detection and identification of apple leaf Alternaria diseases. It has also been observed from the previous work that Support Vector Machines (SVM), Random Forests (RF), XGBoost, and many more are the most common approaches utilized by the researchers. On the other hand, DenseNet, MobileNet, Convolutional Neural Network (CNN), and Vision Transformer are the deep learning approaches utilized by the researchers. Furthermore, we have also given a brief analysis of each approach along with a comparative analysis such as lightweight CNNs and Attention-based mechanisms, Transfer Learning (TL), Localization techniques, Vision Transformer (ViT), and Severity estimation techniques. Emphasizing their methods, datasets, performance metrics, and real-world applications. This study explores the proposed models’ approaches, feature selection and extraction techniques, data capturing conditions, accuracy, types of datasets used in the experiments, and their resources. Our research findings indicate that although DL approaches have significant potential for improving disease management in agriculture. There is a crucial need for a more scalable, robust, and flexible solution to handle numerous agricultural conditions and disease complexities. By methodically and comprehensively analyzing the collected data, this study aims to facilitate valuable resources for researchers aiming to design, develop, and implement DL-based systems for apple leaf disease detection and identification, ultimately contributing to sustainable agriculture and improved food security.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109593"},"PeriodicalIF":7.7,"publicationDate":"2024-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661364","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":"Multi-model fusion method for predicting CO2 concentration in greenhouse tomatoes","authors":"Jianjun Guo ,&nbsp;Beibei Zhang ,&nbsp;Lijun Lin ,&nbsp;Yudian Xu ,&nbsp;Piao Zhou ,&nbsp;Shangwen Luo ,&nbsp;Yuhan Zhuo ,&nbsp;Jingyu Ji ,&nbsp;Zhijie Luo ,&nbsp;Shahbaz Gul Hassan","doi":"10.1016/j.compag.2024.109623","DOIUrl":"10.1016/j.compag.2024.109623","url":null,"abstract":"<div><div>With the rapid development of greenhouse agriculture, accurate prediction of environmental parameters such as temperature, humidity, and carbon dioxide concentration is crucial for optimal crop growth. Traditional forecasting models struggle with the nonlinear and complex nature of greenhouse data, leading to challenges in model robustness. This study addresses these issues by proposing a multi-model fusion strategy for predicting CO2 concentration in greenhouse tomatoes. The proposed method integrates wavelet denoising (WT), variational mode decomposition (VMD), and long short-term memory networks (LSTM). This innovative nonlinear ensemble model effectively extracts key time series features and removes noise, while an introduced attention mechanism enhances the model’s focus on essential time steps, improving prediction accuracy. Experimental results demonstrate that the multi-model fusion approach significantly outperforms single models in terms of accuracy and stability, achieving mean absolute error (MAE) and root mean square error (RMSE) of 0.0117 and 0.0194, respectively. The proposed method offers significant advantages for CO2 prediction in greenhouse crops, providing a theoretical basis and technical support for optimizing and controlling greenhouse parameters. This contributes to the advancement of smart agriculture by offering an efficient environmental monitoring and prediction tool. Additionally, the study presents new ideas and technical solutions for addressing similar agricultural environment prediction challenges, optimizing greenhouse environment control strategies, and improving crop production efficiency.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109623"},"PeriodicalIF":7.7,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661767","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":"Spatial interpolators for Delineating management zones to mitigate Mucuna pruriens in sugarcane plantations in the Eastern Amazon","authors":"Luiz Antonio Soares Cardoso ,&nbsp;Paulo Roberto Silva Farias ,&nbsp;João Almiro Corrêa Soares ,&nbsp;Carlos Rodrigo Tanajura Caldeira ,&nbsp;Fábio Júnior de Oliveira","doi":"10.1016/j.compag.2024.109615","DOIUrl":"10.1016/j.compag.2024.109615","url":null,"abstract":"<div><div>Unmanned Aerial Vehicles (UAVs) have emerged as essential tools in precision agriculture, employing aerial photogrammetry concepts to aid producers in various decision-making processes. This study evauates different spatial interpolators to define management zones in sugarcane fields, aiming to control potential infestations by Mucuna pruriens. We collected images using the EbeeSQ UAV, equipped with a multispectral sensor, and calculated vegetation indices, including NDVI, SAVI, NDRE, and GNDVI. Analysis revealed that GNDVI yielded the most favorable results, with a mean value of 0.304 and a coefficient of variation of 11.747 %. Using regular and random sampling grids, we applied Ordinary Kriging (OK) and Support Vector Machine (SVM) interpolators to assess spatial variability across 13 survey zones. The results indicated a Degree of Spatial Dependence averaging 57.197 % and a Moran Index of 0.609, confirming moderate spatial dependence. Cross-validation showed that OK with random sampling outperformed other methods, achieving a Root Mean Square Error (RMSE) of 0.064 and a coefficient of determination (r²) averaging 0.347. Furthermore, the relationship between the Fuzzy Performance Index (averaging 0.069) and Normalized Classification Entropy (averaging 0.077) enabled the creation of management zone maps. These maps effectively identify distinct classes within the study areas, enhancing decision-making for producers in managing velvet bean weed during critical developmental phases.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109615"},"PeriodicalIF":7.7,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661362","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":"StraTracker: A dynamic counting method for growing strawberries based on multi-target tracking","authors":"Qilin An,&nbsp;Yongzhi Cui,&nbsp;Wenyu Tong,&nbsp;Yangchun Liu,&nbsp;Bo Zhao,&nbsp;Liguo Wei","doi":"10.1016/j.compag.2024.109564","DOIUrl":"10.1016/j.compag.2024.109564","url":null,"abstract":"<div><div>Accurately counting fruit in orchards is a critical step for effective digital farming management. However, the variability in fruit size, overlapping shadows, and light interference present significant challenges to applying computer vision during the strawberry growth phase. To address these challenges, we propose StraTracker, a multi-object tracking (MOT) algorithm specifically designed to identify and count strawberries at various growth stages. StraTracker transforms the counting task into a frame-by-frame tracking problem, integrating both motion and appearance features. The algorithm is composed of three key components: a strawberry detector based on YOLOv8n, a feature association module, and a dual-area counting (DC) module. First, the strawberry detector accurately recognizes five growth stages, achieving an average accuracy of 91.93 % at 38.3 FPS. Next, the feature association module, incorporating the Feature Slicing Attention (FSA) and Adaptive Kalman Filtering (AKF) modules, mitigates issues such as light interference, impractical tracking frames, and ID switching (IDs). As a result, StraTracker achieves a Multi-Object Tracking Accuracy (MOTA) of 83.28 % and a Higher-Order Tracking Accuracy (HOTA) of 77.26 %, with only 259 IDs, outperforming existing baseline models. Finally, the DC module categorizes fruit counts based on the unique IDs assigned during tracking. The algorithm’s coefficient of determination (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></mrow></math></span> = 0.91) and GEH of 2.33 indicate a strong correlation between predicted and actual counts. In conclusion, StraTracker offers a promising solution for farmers to optimize planting strategies and develop more precise harvesting plans.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109564"},"PeriodicalIF":7.7,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661773","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":"Convolutional Neural Networks accurately predict soil matric potential from soil, weather, and satellite-derived data","authors":"Carlos Ballester,&nbsp;John Hornbuckle,&nbsp;Brenno Tondato,&nbsp;Rodrigo Filev-Maia","doi":"10.1016/j.compag.2024.109597","DOIUrl":"10.1016/j.compag.2024.109597","url":null,"abstract":"<div><div>Being able to predict soil moisture dynamics offers water managers the possibility to better plan irrigation events and prevent soil moisture deficits from reaching levels that reduce crop production. Machine learning (ML) model predictions can potentially assist farmers in managing irrigation water more efficiently. In this study, we aimed to assess the accuracy of a set of ML models in predicting soil matric potential seven days ahead in gravity-surface irrigated cotton paddocks and evaluate the models’ performance for longer term predictions (14 days). The ML models used past soil moisture, weather, and satellite-derived crop-related data as features for the input parameters. Input data were structured in tuples that were organised following a 20-day ‘window’ approach that ‘slid’ one position forward after each training round. A convolutional neural network (CNN) model outperformed a Long Short-Term Memory, Dense Multilayer Perceptron, and Linear Regression model, the latter of which produced the least accurate predictions. The accuracy of the soil matric potential predictions with the CNN model was stable over time (R² ≥ 0.92 and root mean square deviation ≤ 7.5 kPa). However, less accurate predictions were obtained for a short period after emergence and at crop senescence. This study demonstrates the feasibility of producing accurate predictions of soil matric potential in cotton fields at 0.20 m soil depth with a CNN model, which can be integrated into irrigation decision support systems.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109597"},"PeriodicalIF":7.7,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661363","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 automatic 3D tomato plant stemwork phenotyping pipeline at internode level based on tree quantitative structural modelling algorithm","authors":"Bolai Xin ,&nbsp;Katarína Smoleňová ,&nbsp;Harm Bartholomeus ,&nbsp;Gert Kootstra","doi":"10.1016/j.compag.2024.109607","DOIUrl":"10.1016/j.compag.2024.109607","url":null,"abstract":"<div><div>Phenotypic traits of stemwork are important indicators of plant growing status, contributing to multiple research domains including yield estimation, breeding engineering, and disease control. Traditional plant phenotyping with human work faces serious bottlenecks on labour intensity and time consumption. In recent years, the application of Quantitative Structural Modeling (QSM) together with three-dimensional (3D) sensor-based data acquisition techniques provides a feasible solution towards the automatic stemwork phenotyping. Nevertheless, existing QSM-based pipelines are sensitive towards the point cloud quality, and mostly focus on the phenotyping at plant or organ level. Information at internode level which are closely related to photosynthesis and light absorption was generally overlooked. To this end, a 3D automatic stemwork phenotyping pipeline is developed for tomato plants at both plant and internode level. Coloured point clouds are taken as the sensor input of the pipeline. A semantic segmentation based on PointNet++ was used to detect and localise the stemwork points. To improve the quality of the segmented stemwork point clouds, a density-based refining pipeline is proposed containing three main processes: non-replacement resampling, interference branch removal, and noise removal. A Tree Quantitative Structural Modeling (TreeQSM) algorithm was then applied to the stemwork point cloud to construct a digital reconstruction. The target phenotypic traits were finally calculated from the digital model by employing an internode association process. The proposed phenotyping pipeline was evaluated with a test dataset containing three tomato plant cultivars: Merlice, Brioso, and Gardener Delight. The related rooted mean squared errors of calculated internode length, internode diameters, leaf branching angle, leaf phyllotactic angle, and stem length range from 4.8 to 64.4%. Considering the time consuming manual phenotyping process, the proposed work provides a feasible solution towards the high throughput plant phenotyping, from which facilitates the related research on plant breeding and crop management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109607"},"PeriodicalIF":7.7,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661763","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":"Hedge three-dimensional reconstruction and motion control technology for trimming robot","authors":"Jin Gu ,&nbsp;Bin Zhang ,&nbsp;Yu Wang ,&nbsp;Yawei Zhang","doi":"10.1016/j.compag.2024.109632","DOIUrl":"10.1016/j.compag.2024.109632","url":null,"abstract":"<div><div>Landscaping is an important way to realize carbon neutralization. The prospect of automatic trimming technology in the horticulture industry has received much attention in recent years. Compared with manual trimming, robots still have a large gap in trimming efficiency and functional integrity. The purpose of this study is to accurately obtain the shape parameters of a hedge by reconstructing its three-dimensional model, enabling the robot to have the complete ability to automate trimming, and improving the efficiency of trimming robot. Firstly, a trimming robot prototype system was constructed by using three-dimensional vision detection technology and autonomous motion control technology. Then, we studied the adaptive template matching method which was used for hedge detection, and the three-dimensional reconstruction method based on curvature feature similarity was used to obtain the position and shape parameters of hedge. We propose an adaptive Ant Colony Optimization trajectory planning method combined with point cloud classification strategy that can improve the efficiency of trimming robot. The results of tests show that the mean absolute value of measurement error of the hand-eye system is 3.7 mm, the mean value of the positioning error of the visual recognition is 2.1 mm, and the mean value of the positioning error of the trimming robot system is 3.8 mm. The trimming robot realized the automatic trimming operation of spherical hedge model and actual hedge in laboratory. During the actual trimming test, it demonstrated an average error of 8.2 mm, and its efficiency and reliability in trimming surpassed manual trimming methods. The research suggests that with the continuous improvement of robot technology, the use of trimming robot system in the horticulture industry will gradually become a reality.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109632"},"PeriodicalIF":7.7,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661817","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}