Agricultural remote sensing big data:Management and applications

Big data with its vast volume and complexity is increasingly concerned, developed and used for all professions and trades. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. Agricultural remote sensing is one of the backbone technologies for precision agriculture, which considers within-field variability for site-specific management instead of uniform management as in traditional agriculture. The key of agricultural remote sensing is, with global positioning data and geographic information, to produce spatially-varied data for subsequent precision agricultural operations. Agricultural remote sensing data, as general remote sensing data, have all characteristics of big data. The acquisition, processing, storage, analysis and visualization of agricultural remote sensing big data are critical to the success of precision agriculture. This paper overviews available remote sensing data resources, recent development of technologies for remote sensing big data management, and remote sensing data processing and management for precision agriculture. A five-layer-fifteen- level （FLFL） satellite remote sensing data management structure is described and adapted to create a more appropriate four-layer-twelve-level （FLTL） remote sensing data management structure for management and applications of agricultural remote sensing big data for precision agriculture where the sensors are typically on high-resolution satellites, manned aircrafts, unmanned aerial vehicles and ground-based structures. The FLTL structure is the management and application framework of agricultural remote sensing big data for precision agriculture and local farm studies, which outlooks the future coordination of remote sensing big data management and applications at local regional and farm scale.

Construction and Application Exploration of Smart Agriculture Based on Big Data Technology

Big data finds extensive application and many fields.It brings new opportunities for the development of agriculture.Using big data technology to promote the development of smart agriculture can greatly improve the effect of agricultural planting,reduce the input of manpower and material resources,and lay a solid foundation for the realization of agricultural modernization.In this regard,this paper briefly analyzes the construction and application of smart agriculture based on big data technology,hoping to provide some valuable insights for readers.

Application of Big Data in Agricultural Informationization in Shandong Province under the Background of "Internet +"

This paper explains the basic concepts of " Internet + " and big data,analyzes the main problems in the application of big data technology in agricultural informationization of Shandong Province,summarizes corresponding solutions from the aspects of government guidance,financial input,open sharing of agricultural big data,big data storage and processing,data mining,etc.,and prospects the application trend of big data technology in agricultural informationization to achieve the connotative development of agriculture in Shandong Province.

A Study on the Application of Statistical Analysis Method of Big Data in Economic Management

This paper analyzes the application value of statistical analysis method of big data in economic management from the macro and micro perspectives,and analyzes its specific application from three aspects such as economic trends,industrial operations and marketing strategies.

Hadoop + Spark Platform Based on Big Data System Design of Agricultural Product Price Analysis and Prediction by HoltWinters

In the market of agricultural products, the price of agricultural products is affected by production cost, market supply and other factors. In order to obtain the market information of agricultural products, the price fluctuation can be analyzed and predicted. A distributed big data software platform based on Hadoop, Hive and Spark is proposed to analyze and forecast agricultural price data. Firstly, Hadoop, Hive and Spark big data frameworks were built to store the data information of agricultural products crawled into MYSQL. Secondly, the information of agricultural products crawled from MYSQL was exported to a text file, uploaded to HDFS, and mapped to spark SQL database. The data was cleaned and improved by Holt-Winters (three times exponential smoothing method) model to predict the price of agricultural products in the future. The data cleaned by spark SQL was imported and predicted by improved Holt-Winters into MYSQL database. The technologies of pringMVC, Ajax and Echarts were used to visualize the data.

Construction of Ideological and Political Micro-Courses of Western Economics in Agricultural Economics Major

As a new means and way of combining media convergence and ideological and political education,ideological and political micro-courses play an important role in the process of cultivating the socialist core values of college students and qualified successors of the socialist cause.For western economics as the core course of agricultural economics major,how to construct ideological and political micro-courses in the era of media convergence to give full play to the effective function of micro-courses in ideological and political education in western economics has good practical significance at present.

Research on Innovation Strategy of Supply Chain Management of Agricultural Enterprises Under the Background of Big Data

With the rapid development and widespread application of Big Data technology, the supply chain management of agricultural products enterprises is facing unprecedented reform and challenges. This study takes the perspective of Big Data technology and collects relevant information on the application of supply chain management in 100 agricultural product enterprises through a survey questionnaire. The study found that the use of Big Data can effectively improve the accuracy of demand forecasting, inventory management efficiency, optimize logistics costs, improve supplier management efficiency, enhance the overall level of supply chain management of enterprises, and propose innovative strategies for supply chain management of agricultural products enterprises based on this. Big Data technology brings a new solution for agricultural products enterprises to enhance their supply chain management level, making the supply chain smarter and more efficient.

Paths for Upgrade and Transformation of E-commerce of China's Fresh Agricultural Products Based on Whole Industry Supply Chain

China's e-commerce market scale of fresh agricultural products has reached 1000 billion yuan,and the prospect is bright.However,in the face of such problems as quality control,storage and distribution,and consumption habits,the fresh product e-commerce has great difficulty in development.Internet generates large data,and data are the resources and the core of the Internet and the foundation for survival and development of Internet.This paper analyzed the current situation of the development of fresh agricultural products,and discussed the plight of fresh product e-commerce in scale,profit,non-standard,and network environment.On the basis of the whole industry supply chain,it came up with paths for upgrade and transformation of the fresh product e-commerce with the big data as the core in the context of Internet environment.

Development and Application of Big Data Platform for Garlic Industry Chain

In order to effectively solve the problems which affect the stable and healthy development of garlic industry,such as the uncertainty of the planting scale and production data,the influence factors of price fluctuation is difficult to be accurately analyzed,the difficult to predict the trend of price change,the uncertainty of the market concentration,and the difficulty of the short-term price prediction etc.the big data platform of the garlic industry chain has been developed.Combined with a variety of data acquisition technology,the information collection of influencing factors for garlic industry chain is realized.Based on the construction of the big data technology platform,the real-time synchronous acquisition,efficient storage and analysis of the planting,market,storage,processing,export and logistics information in five provinces and seven counties are realized.The application of the big data platform for garlic industry chain has realized the accurate acquisition of garlic planting area,the price and trend of market circulation and the information of export information,analyzed the fluctuation regulation of garlic price,and also realized the short-term precision prediction of garlic price.

Triple bottom-line consideration of sustainable plant disease management:From economic,sociological and ecological perspectives

Plant disease management plays an important role in achieving the sustainable development goals of the United Nations(UN)such as food security,human health,socio-economic improvement,resource conservation and ecological resilience.However,technologies available are often limited due to different interests between producers and society and lacks of proper understanding of economic thresholds and the complex interactions among ecology,productivity and profitability.A comprehensive synergy and conflict evaluation of economic,sociological and ecological effects with technologies,productions and evolutionary principles as main components should be used to guide sustainable disease management that aims to mitigate crop and economic losses in the short term while maintaining functional farm ecosystem in the long term.Consequently,there should be an increased emphasis on technology development,public education and information exchange among governments,researchers,producers and consumers to broaden the options for disease management in the future.

Research on the Relationship Between Garlic and Young GarlicShoot Based on Big Data

In view of the problems such as frequent fluctuation of garlic price, lack ofefficient forecasting means and difficulty in realizing the steady development of garlicindustry, combined with the current situation of garlic industry and the collected datainformation. Taking Big Data platform of garlic industry chain as the core, using themethods of correlation analysis, smoothness test, co-integration test, and Grangercausality test, this paper analyzes the correlation, dynamic, and causality between garlicprice and young garlic shoot price. According to the current situation of garlic industry,the garlic industry service based on Big Data is put forward. It is concluded that there is apositive correlation between garlic price and young garlic shoot price, and there is a longtermstable dynamic equilibrium relationship between young garlic shoot price and garlicprice fluctuation, and young garlic shoot price can affect garlic price. Finally, it isproposed to strengthen the infrastructure construction of garlic Big Data, increase thetechnological innovation and application of garlic Big Data technology, and promote thesafety and security ability of the whole industry to promote the development of garlicindustry.

Research on Big Data Platform Design in the Context of Digital Agriculture:Case Study of the Peony Industry in Heze City,China

In recent years,China has successfully set up multiple single-product big data platforms.As an indigenous and unique plant in China,the peony contains immense economic returns,strong social benefits,and profound cultural heritage.Its seed oil,as an emerging edible oil,has attracted much attention.Heze city is one of the places optimal for cultivating peonies.In this context,a study of the big data of the peony industry in Heze city bears practical significance.This paper begins with the literature review of big data platforms for the entire industry.Referring to established single-product big data platforms,it reports the results of a case study of the peony industry in Heze city that identify potential difficulties and problems regarding the building of a big data platform for the peony industry that incorporates the five dimensions of service,management,application,resource,and technology.

特色农产品销售评价大数据的弱监督分析方法

针对特色农产品评价大数据多维度分析中,可信标签不足以及挖掘消费者各维度真实情感语义困难等问题。该研究提出了一种基于弱监督训练的深度学习方法。首先,通过主题模型分析大规模评论,提取产品评价主题和关键词。然后,结合句法依存和情感词典为评论生成不同维度的伪标签。最后,构建多标签多分类深度网络,在伪标签上进行弱监督学习。结果表明,该方法在红心柚评论数据集上取得89.2%的准确率和80.3%的F1值,比随机森林算法提升了7.1个百分点的准确率和11.5个百分点的F1值。相比Transformer模型,准确率提高5.6个百分点,F1值提高2.0个百分点,参数量减少了92%。该方法能从海量评论中高效提取产品评价维度和消费者关注点,为完善农产品质量和销售服务提供数据支持。

数字政府建设背景下江苏省农产品质量安全大数据平台构建与应用研究

本文旨在探讨数字政府背景下,如何构建和应用农产品质量安全大数据平台。文中探讨了数字政府与农产品质量安全大数据平台之间的关系。数字政府的概念的提出与建设将为农产品质量安全大数据平台提供有力支持和可靠保障,并对农产品质量安全平台的建设产生了积极影响;对国内外农业大数据发展情况进行了比较和分析,总结了农业大数据融合发展的经验和启示;提出了数字政府背景下农产品质量安全平台建设的总体构想并且详细讨论了平台的架构和大数据分析模型中的关键点。随后,本文还提出了大数据平台运营保障措施与政策建议,以确保平台的顺利运营和发展。最后本文总结了数字政府背景下农产品质量安全大数据平台构建与应用的研究内容,并展望了进一步的研究方向。

大数据时代的农业发展:国际前沿与中国实践

随着信息革命、数据革命的到来,农业生产已经发生了翻天覆地的变化。在新时代,传统农业逐步遇到瓶颈,亟待向着精准农业、智慧农业等新型数字农业发展模式转型。21世纪以来,世界多国探索了农业大数据的十余个领域,发展了一系列数据收集、存储、分析的技术和方法,并取得了显著的应用成效。中国面临着人口多、耕地少等一系列问题,应紧跟技术进步,结合中国国情和农业特征,加快发展农业新质生产力,在农业大数据的基础研究、行业标准、基础设施、供应链管理、金融保险等方面着力,促进农业高质量发展,加快建设农业强国,促进农业农村现代化。

新文科视域下农业院校经济管理类课程高质量建设的困境与出路

课程是人才培养核心的要素,农业院校打造高质量经济管理类课程是落实新文科建设、推进课程提质的重要实践。当前经济管理类课程建设面临一定挑战,即课程目标轻特色、课程内容缺挑战、课程实施少实践、课程评价不合理等。新文科建设所蕴含的守正创新、价值引领、育人导向之于“金课”有重要启发。打造高质量课程,要紧密贴合农业院校经济管理类课程的特殊性,从课程目标、课程内容、课程实施、课程评价等四个方面入手,培养具有扎实理论、高阶思维、丰富经验和“三农”情怀的服务于现代大农业的经济管理类人才。