Support vector machine-based open crop model (SBOCM): Case of rice production in China
Clicks: 429
ID: 62755
2017
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
76.3
/100
429 views
307 readers
Trending
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #8 of 56 articles by views in saudi journal of biological sciences
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM) was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.
| Reference Key |
su2017supportsaudi
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Su, Ying-xue;Xu, Huan;Yan, Li-jiao; |
| Journal | saudi journal of biological sciences |
| Year | 2017 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Education
Biology (General)
Literature (General)
Diseases of the circulatory (Cardiovascular) system
Immunologic diseases. Allergy
social sciences (general)
social sciences
french literature - italian literature - spanish literature - portuguese literature
history of education
english language
National Center for Biotechnology Information
NCBI
NLM
MEDLINE
pubmed abstract
nih
national institutes of health
national library of medicine
pmid:28386178
pmc5372395
doi:10.1016/j.sjbs.2017.01.024
ying-xue su
huan xu
li-jiao yan
|
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.