Computers and Electronics in Agriculture最新文献

{"title":"Nested sequential feed-forward neural network: A cumulative model for crop yield prediction","authors":"","doi":"10.1016/j.compag.2024.109562","DOIUrl":"10.1016/j.compag.2024.109562","url":null,"abstract":"<div><div>This paper contends that framing crop yield prediction as a time-series problem imposes significant limitations. The varying climatic conditions, along with the distinct time frames associated with different stages of crop cultivation – such as sowing, vegetation growth, flowering, and harvest – present substantial challenges for accurately predicting crop yields. Additionally, the evolving climatic conditions over the years further complicate the prediction process. To address these challenges, this study introduces a novel perspective termed the ’Time-Dimension Based (TDB) Problem,’ offering a conceptual framework that redefines how crop yield prediction should be approached. The TDB framework guides the modeling architecture into two layers: one for capturing the varying climatic conditions and the other for accumulating their impact on crops to determine the final yield. To implement this approach, the paper introduces the ”Nested Sequential Feed-Forward Neural Network (NSFFNet),” a novel neural network architecture. NSFFNet features key components, including an innovative ’Nested Sequential Feed-Forwarding of Inputs’ using feed-forward neural network for capturing Earth’s climatic patterns over time, and a ’Neural Cache Layer’ that utilizes cache memory to accumulate the cumulative impact of these patterns on crop yield. To validate this approach, a comprehensive evaluation of NSFFNet was conducted against traditional time-series models. The model was assessed for accuracy, generalizability, and robustness, particularly in estimating yields during drought years. NSFFNet consistently outperforms established models like RNN, 1D CNN, LSTM, GRU, and Transformer. These findings suggest that redefining crop yield prediction as a TDB problem is a highly effective strategy.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561125","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":"Integrating remote sensing assimilation and SCE-UA to construct a grid-by-grid spatialized crop model can dramatically improve winter wheat yield estimate accuracy","authors":"","doi":"10.1016/j.compag.2024.109594","DOIUrl":"10.1016/j.compag.2024.109594","url":null,"abstract":"<div><div>Grain yield estimation remains a focal point in agricultural research. It’s well known that crop models have very high accuracy in field application, but their scalability to a regional level encounters formidable constraints attributed to stringent input parameter demands, challenges in data acquisition, and complexities in parameter calibration. In a concerted effort to overcome these aforementioned challenges, this study endevours to formulate a spatialized crop growth model, organized grid by grid, propelled by a myriad of data sources encompassing diverse remote sensing and statistical inputs. Our approach involves the integration of a machine learning technique—the shuffled complex evolution algorithm (SCE-UA) to propose an automatic parameter optimization method for model calibration, alongside two remote sensing assimilation methods: a four-dimensional variational assimilation algorithm (4Dvar) and ensemble Kalman filter (Enkf) to optimising model trajectories to improve crop yield estimation accuracy. This innovative methodology addresses the intricacies associated with regional-scale simulation and bridges the gap between the inherent limitations of conventional crop models and the demand for high-precision yield estimations. The results show that: (1) we improved the accuracy of the regional crop model from 0.53 to 0.94 for the coefficient of determination (R²) and from 824.82 kg/ha to 148.48 kg/ha for root mean square error (RMSE), which greatly improved the accuracy of winter wheat yield estimation; (2) after comparing different optimization and assimilation strategies, the simulation strategy of complex shuffling algorithm (SCE-UA) combined with the four-dimensional variational algorithm (4Dvar) can enable the grid-by-grid model to estimate yield to achieve the highest simulation accuracy, with R² of 0.94 and RMSE of 148.48 kg/ha; (3) we evaluated the simulation effectiveness of the algorithm and discuss the shortcomings and uncertainties of the grid-by-grid model. This study has important practical implications for the development of spatialized models for estimating winter wheat yields and bolstering our capacity for informed decision-making in the realm of food production and agricultural management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552660","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":"Generalized few-shot learning for crop hyperspectral image precise classification","authors":"","doi":"10.1016/j.compag.2024.109498","DOIUrl":"10.1016/j.compag.2024.109498","url":null,"abstract":"<div><div>Hyperspectral remote sensing technology, with its advantage of acquiring a substantial amount of spectral information across different bands, has provided a robust tool for crop monitoring and management in the agricultural field. However, a prevalent challenge persists, namely the limited number of labels required for crop classification. We propose a new method named Generalized Few-Shot Learning (GFSL) to address the small-sample problem and get better classification of crops. The proposed GFSL first maps the embedding features extracted by a convolutional neural network to a Hilbert space by an implicit nonlinear mapping with a kernel trick. Then, GFSL maximizes the kernel similarity between each sample and its class mean as much as possible, and minimizes the kernel similarity between each sample and the means of other classes as much as possible at the same time. To give a more meaningful balance between intra-class similarity and inter-class similarity, GFSL defines the negative of intra-class similarity plus the logarithm of the sum of exponential functions of inter-class similarities as the loss function. We conducted experiments on three publicly available crop hyperspectral datasets: WHU-Hi-HanChuan, Salinas, and Indian Pines, and results show that the proposed approach exhibits an improvement in classification accuracy of 11.46%, 6.86%, and 14.49% on the three datasets, respectively, in comparison to some state-of-the-art methods, which demonstrates the superiority of the proposed method for crop hyperspectral image classification with limited training samples. The Python source code is available at <span><span>https://github.com/kkcocoon/GFSL</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142561126","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":"Visual large language model for wheat disease diagnosis in the wild","authors":"","doi":"10.1016/j.compag.2024.109587","DOIUrl":"10.1016/j.compag.2024.109587","url":null,"abstract":"<div><div>Early detection of symptoms in wheat plants is crucial for mitigating disease effects and preventing their spread. Prompt phytosanitary treatment minimizes yield losses and enhances treatment efficacy. In recent years, numerous image analysis-based methodologies for automatic disease identification have been developed, with Convolutional Neural Networks (CNNs) achieving notable success in visual classification tasks. The existing methods often lack the necessary intelligence and reasoning for real-world applications. This study introduces an advanced wheat disease diagnosis approach using a Visual Language Model (VLM), named the Wheat Disease Language Model (WDLM). The WDLM first leverages the modified Segment Anything Model (SAM) to isolate key wheat features from complex wild environments. To enhance the logical reasoning abilities, the WDLM integrates a reasoning chain to generate clear, reasoned explanations for its diagnosis. By employing dedicated prompt engineering, this study establishes the Wheat Disease Semantic Dataset (WDSD) to fine-tune the VLM. The WDSD, which includes a diverse set of wheat images from various sources, bridges the gap between advanced VLM technology and wheat pathology. Tailored with task-specific data, the WDLM demonstrates superior intelligence by providing accurate classification of wheat diseases and suggesting potential treatment options. Compared to CNN-based models, Transformer-based models, and other VLMs, the WDLM shows improved performance in various scenarios. Integrated with mobile applications, the WDLM approach is readily applicable in the field, representing a promising advancement in the intelligent diagnosis of wheat diseases.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552659","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":"Research on human-guided active following mode with 3D spatial relative positioning for vehicles in hilly and mountainous orchards","authors":"","doi":"10.1016/j.compag.2024.109590","DOIUrl":"10.1016/j.compag.2024.109590","url":null,"abstract":"<div><div>Hilly mountainous orchards are an important agricultural scenario, which have narrow spaces that are only suitable for low-speed operations with small-size agricultural machinery. Manned agricultural machinery not only reduces the vehicle’s carrying capacity but also affects the driver’s safety due to the uneven terrain. To address these issues, a human-guided active following mode with 3D spatial relative positioning method was proposed in this study where human motion can be sensed by the vehicle that can actively move towards them. The complex 3D spatial relationship with relative distance, relative angle and vehicle attitude between the guiding-person and following-vehicle was calculated in the relative coordinate system by ultra-wideband positioning techniques. An active following controller is designed, which is simulated and field experimented on a six-wheel off-road chassis. The results indicate that larger desired distances lead to larger trajectory errors. In the space keeping field experiment, the root mean square error of relative distance and relative angle was 0.045 m and 2.591° when the optimal desired distance was set to 1 m and the optimal desired angle was set to 0°. The following-vehicle and guiding-person maintain synchronized motion, wherein the following-vehicle can accomplish acceleration, deceleration and braking based on the motion state of the guiding-person. This research has the potential to free farmers from operating vehicles and concentrate on other agricultural tasks.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539069","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":"Security threats to agricultural artificial intelligence: Position and perspective","authors":"","doi":"10.1016/j.compag.2024.109557","DOIUrl":"10.1016/j.compag.2024.109557","url":null,"abstract":"<div><div>In light of their remarkable predictive capabilities, artificial intelligence (AI) models driven by deep learning (DL) have witnessed widespread adoption in the agriculture sector, contributing to diverse applications such as enhancing crop management and agricultural productivity. Despite their evident benefits, the integration of AI in agriculture brings forth security risks, a concern further exacerbated by the comparatively lower security awareness among agriculture stakeholders. This position paper endeavors to amplify the security consciousness among stakeholders (e.g., end-users such as farmers and governmental bodies) engaged in the implementation of AI systems within the agricultural sector. In our systematic categorization of security threats to AI systems, we delineate three primary avenues of attack: (1) Adversarial Example Attacks, (2) Poisoning Attacks, and (3) Backdoor Attacks. Adversarial example attacks manipulate inputs during the model’s inference phase to induce incorrect predictions. Poisoning attacks corrupt the training data, compromising the model’s availability by indiscriminately degrading its performance on legitimate inputs. Backdoor attacks, typically introduced during the training phase, undermine the model’s integrity, enabling attackers to trigger specific behaviors or outputs through predetermined hidden patterns. An example of compromising AI integrity for malicious purposes is DeepLocker, highlighted by IBM researchers. A detailed examination of attacks in each category is provided, emphasizing their tangible threats to real-world agricultural applications. To illustrate the practical implications, we conduct case studies on specific agricultural applications, focusing on precise irrigation schedules and plant disease detection, utilizing authentic agricultural datasets. Comprehensive countermeasures against each attack type are presented to assist agriculture stakeholders in actively safeguarding their AI applications. Additionally, we address challenges inherent in securing agriculture AI and offer our perspectives on mitigating security threats in this context. This work aims to equip agriculture stakeholders with the knowledge and tools necessary to fortify their AI systems against evolving security challenges. The artifacts of this work are released at <span><span>https://github.com/garrisongys/Casestudy</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539074","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":"Using tri-axial accelerometers data to predict behavior activity of grazing donkeys","authors":"","doi":"10.1016/j.compag.2024.109582","DOIUrl":"10.1016/j.compag.2024.109582","url":null,"abstract":"<div><div>Precision livestock farming technologies represent important tools for farmers to remotely monitor animal behavior while grazing. In this study, three important activities of donkeys at pasture (grazing, walking, and resting) were assessed by analyzing data collected with a tri-axial accelerometer using both univariate and multivariate statistical methods. Eleven donkeys were equipped with the accelerometer attached to their collars. The device was programmed to record acceleration values every second across the three Cartesian directions. Data were organized into various time intervals known as epochs: 15, 30, 60, 90, 120, 150, and 180 s. Raw data were processed for each epoch to compute 12 variables: three mean values, three variances, three inverted coefficients of variation and three across-axes values.<!--> <!-->Univariate ANOVA was conducted to test the effect of behavioral activities (grazing, walking, and resting) on each of the twelve variables. Canonical discriminant analysis (CDA) was subsequently employed to differentiate the three activities and assign time epochs to the corresponding behavior. In the ANOVA model, the behavior factor was significant (<em>P</em>-value &lt; 0.01) for all twelve variables, although grazing and resting did not show significant differences in the inverted coefficients of variation. CDA effectively distinguished the three behavioral activities (Hotelling’s <em>t</em>-test &lt; 0.001) across all epochs. The best performances in assigning time points to the correct behavior were achieved with longer time epochs, such as 120 and 150 s, resulting in overall errors of 9 and 10 %, respectively. During these epochs, the overall accuracy reached approximately 91 and 90 %. The tri-axial accelerometers data, analyzed using multivariate statistical techniques, successfully classified the behavior of donkeys at pasture, thereby providing valuable insights for farmers.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538530","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":"Wolfberry recognition and picking-point localization technology in natural environments based on improved Yolov8n-Pose-LBD","authors":"","doi":"10.1016/j.compag.2024.109551","DOIUrl":"10.1016/j.compag.2024.109551","url":null,"abstract":"<div><div>Recognition and localization technology for wolfberries is a significant research issue in agriculture. The growth environment of wolfberries, including branch and leaf occlusion, dense complexity, and diverse fruit morphology, presents significant challenges for recognition and positioning. Current algorithms for wolfberry recognition and localization suffer from low accuracy, limited applicability, and complex processes, failing to meet the demand for precise and robust smart harvesting. This paper introduces an enhanced Yolov8n-Pose-LBD keypoint detection algorithm inspired by pose estimation. First, a large selection kernel network (LSKNet) was integrated into the bottleneck module of the four C2F gradient routing modules in the backbone, and the C2F-LB structure was recustomized. This structure enhances the dynamic receptive fields and spatial selection mechanisms to better capture wolfberry features. Second, the adaptive point-sampling operator Dysample replaces Upsample, enhancing detection efficiency while reducing GPU memory usage. Additionally, new small target detection layers were designed to effectively detect small wolfberries. Finally, the WIoUv3 loss function was employed to enhance model fitting accuracy. The experimental results indicate that the proposed Yolov8n-Pose-LBD model, with only 3.04 M parameters, achieves high accuracy, recall, and mAP50 in wolfberry detection, reaching 89 %, 86.1 %, and 92.7 %, respectively. These improvements over Yolov8n-Pose were 3.6 %, 6.1 %, and 4.9 %, respectively. For picking-point localization, precision, recall, mAP50, and mAP50-95 were 85.8 %, 82.3 %, 86.4 %, and 85.7 %, respectively, reflecting increases of 2.8 %, 3.5 %, 3 %, and 3.3 %. The model’s prediction errors in this study were less than four pixels for horizontal, vertical, and Manhattan distances in picking-point localization. In conclusion, this algorithm efficiently detected wolfberry targets and identified pickable points, even in complex environments, providing crucial technical support for the subsequent intelligent harvesting of wolfberries.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539065","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":"Investigating agricultural drought in Northern Italy through explainable Machine Learning: Insights from the 2022 drought","authors":"","doi":"10.1016/j.compag.2024.109572","DOIUrl":"10.1016/j.compag.2024.109572","url":null,"abstract":"<div><div>Agricultural drought is a complex natural hazard involving multiple variables and has garnered increasing attention for its severe threat to food security worldwide. In the context of climate change and the increased occurrence of drought events, it is crucial to monitor drought drivers and progression to plan the subsequent efforts in drought prevention, adaptation, and migration. However, previous studies on agricultural drought often focused on precipitation or evapotranspiration, overlooking other potential drivers related to crop drought stress. Additionally, macro-level analyses of drought-driving mechanisms struggle to reveal the underlying contexts of varying drought intensities. Northern Italy is one of the most important agricultural regions in Europe and is also a hotspot affected by extreme climate events in the world. In the summer of 2022, an extreme drought struck Europe once again, causing significant damage to the agricultural regions of Northern Italy. However, no studies to date have revealed the potential impacts and extent of extreme drought on this crucial agricultural area at a regional scale. Therefore, a comprehensive understanding of agricultural drought still requires further clarification and differentiated driver analysis. This study proposed a novel framework to comprehensively monitor agricultural drought with ensemble machine learning by constructing an integrated agriculture drought index (IADI) with remote sensing-related data including meteorology, soil, geomorphology, and vegetation conditions. Additionally, the Shapley Additive Explanation (SHAP) explainable model was applied to reveal the driving mechanism behind the drought event that occurred in northern Italy in the summer of 2022. Results indicated that the proposed explainable ensemble machine learning model with multi-source remote sensing products could effectively depict the evolution of agricultural drought with spatially continuous maps on an 8-day scales. The SHAP analysis demonstrated that the extreme and severe agricultural drought in the summer of 2022 was closely related to meteorological indicators especially precipitation and land surface temperature, which contributed 68.88% to the drought. Moreover, the new findings also highlighted that soil properties affected the agricultural drought with a contribution of 28.3%. Specifically, in the case of moderate and slight drought conditions, higher clay and soil organic carbon (SOC) content contribute to mitigating drought effects, while sandy and silty soils have the opposite effect, and the contributions from soil texture and SOC are more significant than precipitation and land surface temperature. The proposed research framework could effectively contribute to improving the methodology in agricultural drought research, potentially bringing more instructive insights for drought prevention and mitigation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539067","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":"Path planning of greenhouse electric crawler tractor based on the improved A* and DWA algorithms","authors":"","doi":"10.1016/j.compag.2024.109596","DOIUrl":"10.1016/j.compag.2024.109596","url":null,"abstract":"<div><div>To improve the intelligence level and the navigation efficiency of electric crawler tractors in facility greenhouses, this paper proposes a path planning algorithm based on the fusion of the improved A* algorithm and the DWA algorithm. The weight coefficients are integrated into the heuristic function of the A* algorithm, the key point selection strategy is improved, and the second-order Bessel curves are used to smooth the path trajectories. Besides, the DWA algorithm is integrated, and the key point of global paths planned by the improved A* algorithm is taken as an interpolation point. This addresses the issue that the traditional A* algorithm needs to search many nodes and has a low computational efficiency, with many path turning points and unsmooth paths. The results of simulation experiments proved that the improved A* algorithm is less time-consuming and obtains more smoother path than the Dijkstra, RRT, and traditional A* algorithms. Meanwhile, tests in a facility greenhouse show that the electric crawler tractor can realize autonomous navigation and obstacle avoidance, with a maximum lateral deviation of 11.20 cm and a maximum heading deviation of 13°, which can meet the requirements of actual operation in facility greenhouses.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539071","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}