KSUSoy YieldCalc: an innovative native Android app to estimate soybean yield before harvest using conventional approach

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dc.contributor.author Bandyopadhyay, Tania en_US
dc.date.accessioned 2015-04-20T14:44:49Z
dc.date.available 2015-04-20T14:44:49Z
dc.date.issued 2015-04-20
dc.identifier.uri http://hdl.handle.net/2097/18951
dc.description.abstract This report considers a native Android application called “KSUSoy YieldCalc” that assists in yield estimation of soybean before harvest following conventional approach. Android is one of the most popular installed base of any mobile platform, powering many mobile devices in more than 190 countries for users of diverse economic backgrounds, making it more popular than iOS devices (Android developers n.d.).The project “KSUSoy YieldCalc” adopted the Android platform as its base to serve farmers, agronomists, and consultants and deliver performance to save time and enhance farmers’ their confidence. The native application uses “conventional approach” of estimation of yield for calculations and eliminates the need for having Internet connection to access, thereby increasing the application’s flexibility. The project utilized Android Software Development Kit (SDK) as its development platform with extensive Java and Extensible Markup Language (XML) coding. The Department of Agronomy at Kansas State University (KSU) tested the application with promising results. Dr. Ignacio Ciampitti of the Department of Agronomy at KSU currently demonstrates the application to farmers. User feedback has been very satisfactory to date. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Soybean en_US
dc.subject Android app en_US
dc.subject KSUSoy YieldCalc en_US
dc.subject Estimate of yield before harvest en_US
dc.subject Computing and Information Sciences en_US
dc.subject Agronomy en_US
dc.title KSUSoy YieldCalc: an innovative native Android app to estimate soybean yield before harvest using conventional approach en_US
dc.type Report en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Computing and Information Sciences en_US
dc.description.advisor Daniel Andresen en_US
dc.subject.umi Computer Science (0984) en_US
dc.date.published 2015 en_US
dc.date.graduationmonth May en_US


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