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Statistical Estimation of Crop Management Zones from Multi-Year Yield Data and the Oada Api Framework- [electronic resource]
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Statistical Estimation of Crop Management Zones from Multi-Year Yield Data and the Oada Api Framework- [electronic resource]
자료유형  
 학위논문
Control Number  
0016932658
International Standard Book Number  
9798379835002
Dewey Decimal Classification Number  
630
Main Entry-Personal Name  
Layton, Alexander.
Publication, Distribution, etc. (Imprint  
[S.l.] : Purdue University., 2021
Publication, Distribution, etc. (Imprint  
Ann Arbor : ProQuest Dissertations & Theses, 2021
Physical Description  
1 online resource(129 p.)
General Note  
Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
General Note  
Advisor: Krogmeier, James.
Dissertation Note  
Thesis (Ph.D.)--Purdue University, 2021.
Restrictions on Access Note  
This item must not be sold to any third party vendors.
Summary, Etc.  
요약Precision agriculture equipment enables treating different areas of a field differently (i.e., site-specific management). The first part of this work presents an algorithm for inferring the management zones of fields based on multiple years' yield data. It seeks regions that correspond to the same underlying yield distribution. Zones are assumed to be the same each year, but their distributions are allowed to change year-to-year to account for variability. Zones are estimated using stochastic expectation maximization and maximization of the posterior marginals. The underlying assumption is that the yields corresponding to a given zone will behave similarly, and are drawn from the same distribution. This requires only the yield data automatically collected during harvest. This method requires no crop-specific calibration.The second part of this work presents the Open Ag Data Alliance (OADA) Application Programming Interface (API) framework. It is a generic specification that can be used by third parties' APIs to reduce the complexity of interoperating with multiple entities. This is especially useful in intercloud scenarios, for example, moving data between a farmer, a processor, and a distributor. Several existing standards that were leveraged are identified, the graph-based data representation is illustrated, and key API specifications and features are highlighted. Some of the contributions of OADA include user-centric Representational State Transfer (REST) so users can select API clients, resource meta-data stored externally to the resource, live data graphs via change feeds, intercloud data push, and format indifference. A reference implementation is presented and use cases are demonstrated.
Subject Added Entry-Topical Term  
Application programming interface.
Subject Added Entry-Topical Term  
Agricultural production.
Subject Added Entry-Topical Term  
Histograms.
Subject Added Entry-Topical Term  
Protocol.
Subject Added Entry-Topical Term  
Soybeans.
Subject Added Entry-Topical Term  
Corn.
Subject Added Entry-Topical Term  
Agriculture.
Added Entry-Corporate Name  
Purdue University.
Host Item Entry  
Dissertations Abstracts International. 85-01B.
Host Item Entry  
Dissertation Abstract International
Electronic Location and Access  
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Control Number  
joongbu:641505
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