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Titlebook: Computer and Computing Technologies in Agriculture VIII; 8th IFIP WG 5.14 Int Daoliang Li,Yingyi Chen Conference proceedings 2015 IFIP Inte

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发表于 2025-3-21 18:43:45 | 显示全部楼层 |阅读模式
书目名称Computer and Computing Technologies in Agriculture VIII
副标题8th IFIP WG 5.14 Int
编辑Daoliang Li,Yingyi Chen
视频videohttp://file.papertrans.cn/235/234350/234350.mp4
概述Includes supplementary material:
丛书名称IFIP Advances in Information and Communication Technology
图书封面Titlebook: Computer and Computing Technologies in Agriculture VIII; 8th IFIP WG 5.14 Int Daoliang Li,Yingyi Chen Conference proceedings 2015 IFIP Inte
描述This book constitutes the refereed post-conference proceedings of the 8th IFIP WG 5.14 International Conference on Computer and Computing Technologies in Agriculture, CCTA 2014, held in Beijing, China, in September 2014. The 81 revised papers included in this volume were carefully selected from 216 submissions. They cover a wide range of interesting theories and applications of information technology in agriculture, including intelligent sensing, monitoring and automatic control technology; key technology and models of the Internet of things;intelligent technology for agricultural equipment; computer vision; computer graphics and virtual reality; computer simulation, optimization and modeling; cloud computing and agricultural applications; agricultural big data; decision support systems and expert systems; 3s technology and precision agriculture;quality and safety of agricultural products: detection and tracing technology; and agricultural electronic commerce technology.
出版日期Conference proceedings 2015
关键词computers in agriculture; computer vision; decision support systems; image processing; information syste
版次1
doihttps://doi.org/10.1007/978-3-319-19620-6
isbn_softcover978-3-319-79282-8
isbn_ebook978-3-319-19620-6Series ISSN 1868-4238 Series E-ISSN 1868-422X
issn_series 1868-4238
copyrightIFIP International Federation for Information Processing 2015
The information of publication is updating

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发表于 2025-3-21 20:20:00 | 显示全部楼层
Research on Data Sharing Model Based on Cluster,udy the advantage and disadvantage of the three current popular sharing model, and propose a sharing model based on cluster according to the reality of the digital campus. By the contrast and analysis of performance, the result show that the model can meet the requirements of the data sharing well.
发表于 2025-3-22 02:17:37 | 显示全部楼层
https://doi.org/10.1007/978-3-319-19620-6computers in agriculture; computer vision; decision support systems; image processing; information syste
发表于 2025-3-22 06:57:16 | 显示全部楼层
978-3-319-79282-8IFIP International Federation for Information Processing 2015
发表于 2025-3-22 11:15:03 | 显示全部楼层
Computer and Computing Technologies in Agriculture VIII978-3-319-19620-6Series ISSN 1868-4238 Series E-ISSN 1868-422X
发表于 2025-3-22 14:56:34 | 显示全部楼层
https://doi.org/10.1007/978-94-007-7180-2a in agricultural science. The paper analyses current situation and existing problems on data sharing in our country, based on expounding the connotation of agricultural scientific data. Then some countermeasures and suggestions are put forward to promote the level of the agricultural scientific data sharing.
发表于 2025-3-22 19:57:39 | 显示全部楼层
发表于 2025-3-22 22:29:25 | 显示全部楼层
https://doi.org/10.1007/978-3-540-71659-4ndidate plant materials are urgently needed. Final yield is a significant agricultural trait in rice breeding. In the screening and evaluation of the rice varieties, measuring and evaluating rice yield is essential. Conventional means of measuring rice yield mainly depend on manual determination, wh
发表于 2025-3-23 04:45:57 | 显示全部楼层
https://doi.org/10.1007/978-3-540-71659-4dopt side-view imaging technology. When satellites imaging with a side perspective, it would inevitably cause the degradation of image quality, like spatial resolution reduction, image deformation incensement, and the reduction of positioning accuracy. In addition, with the increase of the side pers
发表于 2025-3-23 09:15:06 | 显示全部楼层
Only the Architecture You Need,g hyperspectral remote sensing, through the different levels of the incidence of wheat take-all canopy spectral reflectance data collection analysis and processing, using support vector machine(SVM) classification method to build Wheat Take-all disease level prediction model for the prediction and p
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