According to the objective reality in Yunnan Province,such as great difference of weather,a great proportion of minorities,technological backwardness of agricultural production,low conversion rate of research accompli...According to the objective reality in Yunnan Province,such as great difference of weather,a great proportion of minorities,technological backwardness of agricultural production,low conversion rate of research accomplishments,and a limited number of agricultural technical personnel,ASP.NET technique and SQL Server 2005 database technique are adopted.Database platform of county agricultural production technology in Yunnan Province is established by using B/S structure.This platform includes presentation layer,application layer,and data layer,involving regional information,technology column information,technology classification information,technology content and other databases.It has six functional modules,namely information browse,system management,regional maintenance,technical section maintenance,category maintenance and technical information release,integrating crop cultivation,livestock breeding,economic forest management,plant protection,agricultural products processing,agricultural machinery use and other agricultural technical information.This platform can exchange information dynamically with the client,perform the query request from users,and send the result to users.This database platform has friendly interface,profuse information,high pertinency and so on,which offers rich and reliable information resources to farmers,agricultural technical personnel,and government.At present,this platform has been popularized in some areas of Yunnan Province and has obtained good results.展开更多
Apricot has a long history of cultivation and has many varieties and types. The traditional variety identification methods are timeconsuming and labor-consuming, posing grand challenges to apricot resource management....Apricot has a long history of cultivation and has many varieties and types. The traditional variety identification methods are timeconsuming and labor-consuming, posing grand challenges to apricot resource management. Tool development in this regard will help researchers quickly identify variety information. This study photographed apricot fruits outdoors and indoors and constructed a dataset that can precisely classify the fruits using a U-net model (F-score:99%), which helps to obtain the fruit's size, shape, and color features. Meanwhile, a variety search engine was constructed, which can search and identify variety from the database according to the above features. Besides, a mobile and web application (ApricotView) was developed, and the construction mode can be also applied to other varieties of fruit trees.Additionally, we have collected four difficult-to-identify seed datasets and used the VGG16 model for training, with an accuracy of 97%, which provided an important basis for ApricotView. To address the difficulties in data collection bottlenecking apricot phenomics research, we developed the first apricot database platform of its kind (ApricotDIAP, http://apricotdiap.com/) to accumulate, manage, and publicize scientific data of apricot.展开更多
The rapid growth of structured data has presented new technological challenges in the research fields of big data and relational database. In this paper, we present an efficient system for managing and analyzing PB le...The rapid growth of structured data has presented new technological challenges in the research fields of big data and relational database. In this paper, we present an efficient system for managing and analyzing PB level structured data called Banian. Banian overcomes the storage structure limitation of relational database and effectively integrates interactive query with large-scale storage management. It provides a uniform query interface for cross-platform datasets and thus shows favorable compatibility and scalability. Banian＇s system architecture mainly includes three layers：（1） a storage layer using HDFS for the distributed storage of massive data;（2） a scheduling and execution layer employing the splitting and scheduling technology of parallel database; and（3）an application layer providing a cross-platform query interface and supporting standard SQL. We evaluate Banian using PB level Internet data and the TPC-H benchmark. The results show that when compared with Hive, Banian improves the query performance to a maximum of 30 times and achieves better scalability and concurrency.展开更多
基金Supported by the National Science & Technology Pillar Program during the Eleventh Five-Year Plan Period(2006BAD10A14)
文摘According to the objective reality in Yunnan Province,such as great difference of weather,a great proportion of minorities,technological backwardness of agricultural production,low conversion rate of research accomplishments,and a limited number of agricultural technical personnel,ASP.NET technique and SQL Server 2005 database technique are adopted.Database platform of county agricultural production technology in Yunnan Province is established by using B/S structure.This platform includes presentation layer,application layer,and data layer,involving regional information,technology column information,technology classification information,technology content and other databases.It has six functional modules,namely information browse,system management,regional maintenance,technical section maintenance,category maintenance and technical information release,integrating crop cultivation,livestock breeding,economic forest management,plant protection,agricultural products processing,agricultural machinery use and other agricultural technical information.This platform can exchange information dynamically with the client,perform the query request from users,and send the result to users.This database platform has friendly interface,profuse information,high pertinency and so on,which offers rich and reliable information resources to farmers,agricultural technical personnel,and government.At present,this platform has been popularized in some areas of Yunnan Province and has obtained good results.
基金supported by the Fundamental Research Funds for the Central Non-profit Research Institution of the Chinese Academy of Forestry (Grant No.CAFYBB2020ZY003)the Key S&T Project of Inner Mongolia (Grant No.2021ZD0041-001-002)the Central Public-interest Scientific Institution Basal Research Fund (Grant No.11024316000202300001)。
文摘Apricot has a long history of cultivation and has many varieties and types. The traditional variety identification methods are timeconsuming and labor-consuming, posing grand challenges to apricot resource management. Tool development in this regard will help researchers quickly identify variety information. This study photographed apricot fruits outdoors and indoors and constructed a dataset that can precisely classify the fruits using a U-net model (F-score:99%), which helps to obtain the fruit's size, shape, and color features. Meanwhile, a variety search engine was constructed, which can search and identify variety from the database according to the above features. Besides, a mobile and web application (ApricotView) was developed, and the construction mode can be also applied to other varieties of fruit trees.Additionally, we have collected four difficult-to-identify seed datasets and used the VGG16 model for training, with an accuracy of 97%, which provided an important basis for ApricotView. To address the difficulties in data collection bottlenecking apricot phenomics research, we developed the first apricot database platform of its kind (ApricotDIAP, http://apricotdiap.com/) to accumulate, manage, and publicize scientific data of apricot.
基金supported by the National High-Tech Research and Development (863) Program of China (No. 2012AA012609)
文摘The rapid growth of structured data has presented new technological challenges in the research fields of big data and relational database. In this paper, we present an efficient system for managing and analyzing PB level structured data called Banian. Banian overcomes the storage structure limitation of relational database and effectively integrates interactive query with large-scale storage management. It provides a uniform query interface for cross-platform datasets and thus shows favorable compatibility and scalability. Banian＇s system architecture mainly includes three layers：（1） a storage layer using HDFS for the distributed storage of massive data;（2） a scheduling and execution layer employing the splitting and scheduling technology of parallel database; and（3）an application layer providing a cross-platform query interface and supporting standard SQL. We evaluate Banian using PB level Internet data and the TPC-H benchmark. The results show that when compared with Hive, Banian improves the query performance to a maximum of 30 times and achieves better scalability and concurrency.
