Computers and Electronics in Agriculture最新文献

{"title":"RootEx: An automated method for barley root system extraction and evaluation","authors":"Maichol Dadi,&nbsp;Alessandra Lumini,&nbsp;Annalisa Franco","doi":"10.1016/j.compag.2025.110030","DOIUrl":"10.1016/j.compag.2025.110030","url":null,"abstract":"<div><div>Plant phenotyping plays a crucial role in agricultural research, especially in identifying resilient traits essential for global food security. Quantitative analysis of root growth has become increasingly vital in evaluating a plant’s resilience to abiotic stresses and its efficiency in nutrient and water absorption. However, extracting features from root images presents substantial challenges due to the complexity of root structures, variations in size, background noise, occlusions, clutter, and inconsistent lighting conditions.</div><div>In this study, we introduce “RootEx”, a comprehensive automated approach for extracting barley plant root systems from high-resolution images acquired from 2D root phenotyping systems set up in transparent growing mediums. Our method involves several stages, beginning with preprocessing to identify the Region of Interest (ROI). Subsequent stages utilize deep neural network-based segmentation, skeleton construction, and graph generation to produce detailed representations of root systems stored in RSML format. Notably, our dataset exclusively comprises primary roots without secondary roots or bifurcations, allowing for a focused examination of primary root characteristics and environmental adaptability.</div><div>Evaluation against established methods, RootNav 1.8 and 2.0, reveals significant improvements in root system reconstruction accuracy across various performance indicators. Although RootEx may exhibit slightly lower performance due to the absence of neural network-based tip detection, its advantages include minimal losses in missing root lengths and independence from dedicated training datasets. Our approach effectively mitigates detection errors, providing a reliable tool for precise barley root analysis in agricultural research.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"230 ","pages":"Article 110030"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143201890","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":"Time-varying compressive mechanical characteristics and empirical model construction of loquat (Eriobotrya japonica Lindl.) based on FEM-RSM","authors":"Changsu Xu ,&nbsp;Puzan Zhang ,&nbsp;Rui Song ,&nbsp;Han Tang ,&nbsp;Yunwu Li","doi":"10.1016/j.compag.2025.110062","DOIUrl":"10.1016/j.compag.2025.110062","url":null,"abstract":"<div><div>Loquats (<em>Eriobotrya japonica</em> Lindl.) comprise peel, flesh, and kernel and are commonly used in the food, pharmaceutical, and light industrial fields. The compression issue involves multiple stages from harvesting to postharvest, and understanding the mechanical response of loquats during transient compression processes is crucial for controlling mechanical damage. This study analyzed the route of stress transmission and the evolution of characteristics during compression processes. Comprehensive empirical models for mechanical damage and empirical models with constraint conditions were established to adapt to different mechanical research and development conditions. A three-dimensional model of a loquat was constructed using reverse engineering methods, and a finite element model comprising three parts, namely, peel, flesh, and kernel, was established. The accuracy of the model under compression conditions was validated (maximum error of 6.46 %). Compression mechanical characteristics were compared using four contact materials (aluminum alloy, PVC, ceramics, and rubber), four forces (5, 10, 15, and 20 N), and four angles (0°, 20°, 40°, and 60°). During the compression process, stress concentration occurred at the junction between the flesh and kernel of the loquat. Compared with the other three materials, rubber exhibited a lower equivalent compressive stress on the loquat. The equivalent compressive stress on the loquat was the lowest at a compression angle of 20°. An empirical model considering the interaction between force and angle was developed, along with the empirical models under single-constraint conditions. This study provides insights into the mechanical characteristics of fruit compression caused by harvesting methods and offers solutions for the optimization design of harvesting machinery.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"231 ","pages":"Article 110062"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349060","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":"Dynamic real-time detection for corn kernel breakage rate based on deep learning and sliding window technology","authors":"Qihuan Wang ,&nbsp;Qinghao He ,&nbsp;Dong Yue ,&nbsp;Duanxin Li ,&nbsp;Jianning Yin ,&nbsp;Pengxuan Guan ,&nbsp;Yancheng Sun ,&nbsp;Duanyang Geng ,&nbsp;Zhenwei Wang","doi":"10.1016/j.compag.2025.109926","DOIUrl":"10.1016/j.compag.2025.109926","url":null,"abstract":"<div><div>Currently, the field of intelligent corn harvesting in China lacks effective methods for detecting corn kernel breakage. This paper explores and proposes a corn kernel detection technology that utilizes deep learning and sliding window technology, combined with a specially developed quantitative model, to enable real-time detection of the corn kernel breakage rate. In this study, we quantified the corn kernel mass at various levels of crushing and proposed a quantitative model for the corn kernel breakage rate, which is suitable for real-time computation by a computer vision system. We developed a specialized corn kernel detection device to generate high-quality datasets and retrain our previously proposed corn kernel breakage detection model (BCK-YOLOv7). Subsequently, ablation experiments were conducted to assess the generalization capability of the BCK-YOLOv7 model in corn kernel detection. Furthermore, we analyzed the limitations of single-frame detection through dynamic comparison experiments. To address the instability of single-frame detection results in the corn kernels flow state, we introduced the sliding window technique, which, along with pipeline technology, significantly enhances detection efficiency. Finally, the comprehensive performance of the proposed corn kernel breakage detection technology was validated through systematic testing. The results indicate that the relative error in the detection of the breakage rate remains around 7%, and the detection rate of the technology, when deployed on edge devices, can achieve 22 frames per second (FPS), thereby meeting the requirements for real-time detection of corn kernel breakage rate.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 109926"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143295986","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":"A customized density map model and segment anything model for cotton boll number, size, and yield prediction in aerial images","authors":"Chenjiao Tan ,&nbsp;Jin Sun ,&nbsp;Huaibo Song ,&nbsp;Changying Li","doi":"10.1016/j.compag.2025.110065","DOIUrl":"10.1016/j.compag.2025.110065","url":null,"abstract":"<div><div>The number of cotton bolls is an important phenotyping trait not only for breeders but also for growers. It can provide information on the physiological and genetic mechanisms of plant growth and aid decision-making in crop management. However, traditional visual inspection in the field is time-consuming and laborious. With the application of drones in the agricultural domain, there is promising potential to collect data expediently. In this paper, we integrated the improved Distribution Matching for crowd Counting (DM-Count) and Segment Anything Model (SAM) to predict cotton boll number, size, and yield in aerial images. The cotton plots were first extracted from the raw aerial images using boundaries derived from orthophotos. Then, a convolutional neural network (DM-Count) was introduced as a baseline and customized by replacing the VGG19 backbone and adding a pixel loss. The customized network was first pretrained on ground images and then fine-tuned on aerial images to predict the density map, where the number and locations of cotton bolls can be obtained. The zero-shot foundation model SAM was investigated to segment cotton bolls with the point prompts provided by customized DM-Count. The respective numbers of bolls and segmented pixels were compared for seed cotton yield estimation. The experimental results showed that the customized model obtained a mean absolute error (MAE) of 1.78 per square meter and a mean absolute percentage error (MAPE) of 4.39 % on the testing dataset, with a high correlation between the predicted boll number and ground truth (R² = 0.91). The AP50 of SAM for cotton boll segmentation was 0.63. The segmented masks were used to delineate the boll size differences among the four genotypes, and it was found that the average boll size of Pima was 452 pixels, which was significantly smaller than Acala Maxxa, UA 48 and Tamcot Sphinx. Moreover, the yield estimation using the boll number was better than that using the pixel number, with an R² = 0.70. Combing the boll number and the pixel number can achieve a slightly higher R² of 0.72 for yield estimation. Overall, the customized model can count cotton bolls in aerial images accurately and estimate seed cotton yield effectively, which could significantly benefit breeders in developing genotypes with high yields, as well as help growers in yield estimation and crop management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 110065"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143352801","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-step prediction of dissolved oxygen in fish pond aquaculture using feature reconstruction-based deep neural network","authors":"Yilun Jiang ,&nbsp;Lintong Zhang ,&nbsp;Chuxin Wang ,&nbsp;Linjie Chen ,&nbsp;Wenqing Zhang ,&nbsp;Haiyong Weng ,&nbsp;Limin Xie ,&nbsp;Fangfang Qu","doi":"10.1016/j.compag.2025.109997","DOIUrl":"10.1016/j.compag.2025.109997","url":null,"abstract":"<div><div>Dissolved Oxygen (DO) is a pivotal indicator for sustaining the vitality and productivity of aquatic ecosystems. To empower sophisticated aquaculture management, a novel approach of feature reconstruction integrated with deep neural networks was proposed to predict the future DO trends within fish pond aquaculture with exceptional precision and reliability. The time series data of water quality factors including pH, water temperature, conductivity, turbidity, air temperature, and humidity were obtained synchronously by sensing devices. The sequence of Spearman correlation analysis (SCA), variational mode decomposition (VMD), and convolutional neural networks (CNN) formed the feature reconstruction method (SCA-VMD-CNN, SVC) for feature optimization, decomposition, and spatiotemporal feature extraction, addressing the nonlinear and temporal features of DO data in aquaculture. The Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks were established based on the SVC features for multi-step predicting of DO. Compared with other state-of-the-art methods, the results showed that SVC effectively improved the accuracy of the DNNs by 16.8 %∼19.5 % for multi-step prediction of future DO trends within fish pond aquaculture. The SVC-BiGRU obtained the highest predictive performances with R² of 0.962, 0.934, 0.940 for predicting 1-step, 2-step, and 3-step DO content in the next 15, 30, and 45 min. Our proposed methodology paves a pathway toward dynamic monitoring of DO trends, aimed at improving aquaculture efficiency and reducing risks. It may play an essential role in the near future for time-series analysis in precision aquaculture.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 109997"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143295981","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":"A cloud point methodology for evaluating the integrity risk of arboreal crop during field coverage of agricultural machinery","authors":"Antony Kachappilly ,&nbsp;Rosa Devanna ,&nbsp;Miguel Torres-Torriti ,&nbsp;Fernando Auat Cheein","doi":"10.1016/j.compag.2024.109844","DOIUrl":"10.1016/j.compag.2024.109844","url":null,"abstract":"<div><div>Several countries around the world are currently facing the need to have a fully automated farming process. In the United Kingdom, for example, the government has acknowledged this need and named it <em>farmgate</em>. As part of such process, machinery is designed to traverse the field performing a previously given task, such as harvesting, seeding, herbicide management, among others, following an also previously given path (or way points). However, when traversing, and due to environment layout or vehicle manoeuvres, machinery might collide, damaging itself or affecting the crop’s health. To address this problem, in this work, we develop a methodology for the evaluation of the expected damage in a crop, when a path has been planned for the agricultural machinery. To this end, we use point cloud processing tools that allow us to account the hitting risks per each manoeuvre and, therefore, to make the proper corrections in the path planning process. Hence, after evaluation, we are able to minimise the damage of the crop. Our proposal is tested on an existing and publicly available dataset, named <em>CitrusFarm dataset</em>, using the dimensions of several commercially available tractors, such as John Deere 9R, New Holland T8.435 (76.2 cm SmartTrax), Case IH Magum^TM 400 Rowtrac, and it can be extended to other platforms. The statistical results, show for example, that for the John Deere 9R tractor on the tested field, there is a 49.71% (sequence 04 from the dataset) and 87.27% (sequence 06) of risk of severely damaging the crop, whereas New Holland T8.435 tractor shows 34.34% (sequence 04) and 55.96% (sequence 06) and Case IH Magum^TM 400 Rowtrac shows 33.17%(sequence 04) and 52.41% (sequence 06). A final case study is implemented where our approach is successfully part of the decision process in the placement of waypoints in an olive grove to minimise impacts. The later shows that our methodology can benefit machinery design and path planning for full coverage practices in agriculture.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"231 ","pages":"Article 109844"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349013","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":"Improving winter wheat plant nitrogen concentration prediction by combining proximal hyperspectral sensing and weather information with machine learning","authors":"Xiaokai Chen ,&nbsp;Fenling Li ,&nbsp;Qingrui Chang ,&nbsp;Yuxin Miao ,&nbsp;Kang Yu","doi":"10.1016/j.compag.2025.110072","DOIUrl":"10.1016/j.compag.2025.110072","url":null,"abstract":"<div><div>Timely and accurate prediction of nitrogen (N) status in winter wheat is crucial for guiding precision N management. This study aimed to develop an efficient model for predicting winter wheat plant N concentration (PNC) by integrating proximal hyperspectral sensing data with weather information. Hyperspectral data were collected from six field experiments conducted from 2014 to 2023, which were preprocessed using first-order derivative, log-transformation, and continuum removal methods. Effective spectral bands were selected by least absolute shrinkage and selection operator (LASSO), combined with weather information and analyzed using seven machine learning algorithms. The results indicated that first-order derivative-preprocessed bands combined with Elastic Net Regression provided the best PNC prediction (coefficient of determination (R²) = 0.78, root mean square error (RMSE) = 0.28 % and relative prediction deviation (RPD) = 2.15) among the tested methods. Combining proximal hyperspectral sensing and weather information with machine learning algorithms significantly enhanced winter wheat PNC predictions (R² = 0.79–0.85, RMSE = 0.23–0.27 % and RPD = 2.15–2.56) compared with using proximal hyperspectral sensing (R² = 0.34–0.79, RMSE = 0.28–0.48 % and RPD = 1.23–2.15) alone. This approach offers a promising framework for winter wheat PNC prediction to support precision N management. Future work should focus on developing multi-source data fusion strategies, incorporating unmanned aerial vehicle or satellite hyperspectral sensing and machine learning, for large-scale monitoring of crop N status and N management decision making.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 110072"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143295930","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":"Weighing finishing pigs in motion: A walk-over scale for accurate weight estimation","authors":"François Decarie ,&nbsp;Charles Grant ,&nbsp;Gabriel Dallago","doi":"10.1016/j.compag.2025.110019","DOIUrl":"10.1016/j.compag.2025.110019","url":null,"abstract":"<div><div>Accurate and efficient weight estimation of pigs is crucial for optimizing production, ensuring animal welfare, and making informed decisions in swine farming. Despite technological advancements, obtaining precise individual pig weights remains challenging due to the dynamic nature of pig movement and the stress induced by traditional weighing methods, highlighting the need for innovative, non-invasive solutions. This study presents an automated walk-over scale system that leverages high-frequency load cell data, feature engineering, and machine learning techniques to estimate pig weights in motion, addressing the limitations of traditional weighing methods. The system’s effectiveness was validated in a real-world setting with 50 pigs across 944 walk-throughs, achieving a Root Mean Square Error (RMSE) of 2.87 kg and a Mean Absolute Percentage Error (MAPE) of 2.65% on a 20% pig-wise holdout validation set, demonstrating its potential as a practical solution for non-invasive, accurate weight monitoring in commercial pig farming operations.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 110019"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143296034","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":"Spatio-temporal yield variation and precipitation within a field","authors":"Emmanuela van Versendaal ,&nbsp;Carlos M. Hernandez ,&nbsp;Peter Kyveryga ,&nbsp;Trevor Hefley ,&nbsp;Bradley W. Van De Woestyne ,&nbsp;P.V. Vara Prasad ,&nbsp;Ignacio A. Ciampitti","doi":"10.1016/j.compag.2025.109996","DOIUrl":"10.1016/j.compag.2025.109996","url":null,"abstract":"<div><div>In rainfed systems, precipitation significantly influence crop yield variability across years. Understanding the spatio-temporal yield variation within a field is crucial for enhancing farming production and profitability. This study aims to establish a data pipeline to quantify spatio-temporal yield variation within a field in response to precipitation, using multiple years of yield-precipitation records. Four field case studies – two with maize and two with soybean − each containing at least five years of yield monitor data of the same crop are used to evaluate the proposed data pipeline. Daily precipitation data of each case study were obtained from CHIRPS dataset and used to calculate derived variables representing cumulative precipitation over different periods. Generalized Additive Models (GAMs) were fitted to analyze the within field variation of yield response to cumulative precipitation across all possible cumulative precipitation periods. The optimal cumulative precipitation period was selected based on the Akaike Information Criterion. Spatially varying response parameters derived from the GAM with the optimal cumulative precipitation period were then used to classify the varying yield responses to precipitation within the field using a Classification and Regression Tree methodology. The most relevant outcomes of this study include: (i) the optimal period of cumulative precipitation impacting yield change by field and crop type; (ii) within a field, some areas exhibit greater yield variability than others; and (iii) areas with equal high yield variability within the field may respond differently to precipitation. Future work could include the utilization of the spatio-temporal variation within a field for guiding input recommendations.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"231 ","pages":"Article 109996"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348994","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":"The NBCalCer model for calculating global benefits and costs of nitrogen fertilizer use for cereal cultivation: Model description, uncertainty analysis and validation","authors":"Alfredo Rodríguez ,&nbsp;Hans J.M. van Grinsven ,&nbsp;Rasmus Einarsson ,&nbsp;Arthur H.W. Beusen ,&nbsp;Alberto Sanz-Cobena ,&nbsp;Luis Lassaletta","doi":"10.1016/j.compag.2025.110039","DOIUrl":"10.1016/j.compag.2025.110039","url":null,"abstract":"<div><div>The challenge of achieving food security in a world with a growing population, given decreasing area of good arable land, and climate change impacts, necessitates structural system changes which includes a more efficient and less polluting agricultural practices. Cereals represent the major staple crop to feed the global population, and its production heavily relies on synthetic nitrogen (N) fertilizers. However, increasing fertilizer inputs for higher yields is not sustainable without proper management of soil, crop, inputs, water and nutrients. The NBCalCer tool was developed to evaluate options for a more sustainable cereal cultivation. NBCalCer quantifies long-term yield responses to N inputs and its implications for N budgets, N losses and environmental impacts for global countries. Crop benefits and environmental damages are monetized to assess benefit-cost consequences for the farming sector and society, to determine optimal N inputs for both sufficient grain supply and acceptable N pollution levels.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"231 ","pages":"Article 110039"},"PeriodicalIF":7.7,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348996","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}