Prediction of Sugarcane Yields from Field Records using Regression Modeling
Prediction Of Sugarcane Crop Yield Benefits The Farmer To Get Best Possible Decision Regarding Sugarcane Crop Cultivation. The Purpose Of This Work Is To Identify Possible Relationship Between N, P, K Fertilizer, Water Resource And Planting Densities. The Algorithm Used Is Multiple Regression. The P...
Gespeichert in:
| Veröffentlicht in: | International journal of recent technology and engineering 2019-11, Vol.8 (4), p.1603-1606 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 1606 |
|---|---|
| container_issue | 4 |
| container_start_page | 1603 |
| container_title | International journal of recent technology and engineering |
| container_volume | 8 |
| creator | Kale, Shivani S. Patil, Dr. Preeti S. |
| description | Prediction Of Sugarcane Crop Yield Benefits The Farmer To Get Best Possible Decision Regarding Sugarcane Crop Cultivation. The Purpose Of This Work Is To Identify Possible Relationship Between N, P, K Fertilizer, Water Resource And Planting Densities. The Algorithm Used Is Multiple Regression. The Paper Focuses On The Generation Of Multiple Regression Models For The Dataset Of Sugarcane Crop For Season Adasali, Suru And Preseasonal Method. The Intercept And Slope For Variables Are Calculated And Equation For Each Model Is Generated. Sample Of N,P,K And Other Are Considered For A Period Of 7 Years From 2012 To 2018. Data Of Experimentation Is Collected For Arid Region I.E. Pandharpur, Maharashtra State. |
| doi_str_mv | 10.35940/ijrte.C4174.118419 |
| format | Article |
| fullrecord | <record><control><sourceid>crossref</sourceid><recordid>TN_cdi_crossref_primary_10_35940_ijrte_C4174_118419</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>10_35940_ijrte_C4174_118419</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1599-54c16d23aea9447744f6371b4de92149fb134594a9d8a318f3863da9274a55913</originalsourceid><addsrcrecordid>eNpNkM1OwzAQhC0EElXpE3DxCyRks5vYPqKIAqIIxM-Bk-XGdpSqjZHdHnh70pQDp50daUaaj7FrKHKsFBU3_SbuXd4QCMoBJIE6Y7OyFCJDKeT5P33JFiltiqIArIGwnrGn1-hs3-77MPDg-fuhM7E1g-NfvdvaxH0MO748av7m2hBH65D6oRu_LrqUjrnnYN129K7YhTfb5BZ_d84-l3cfzUO2erl_bG5XWQuVUllFLdS2ROOMIhKCyNcoYE3WqRJI-TUgjcOMstIgSI-yRmtUKchUlQKcMzz1tjGkFJ3X37HfmfijodATEj0h0RMSfUKCvxfIVO8</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Prediction of Sugarcane Yields from Field Records using Regression Modeling</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Kale, Shivani S. ; Patil, Dr. Preeti S.</creator><creatorcontrib>Kale, Shivani S. ; Patil, Dr. Preeti S. ; Assistant professor, KIT, Kolhapur, Maharashtra, India</creatorcontrib><description>Prediction Of Sugarcane Crop Yield Benefits The Farmer To Get Best Possible Decision Regarding Sugarcane Crop Cultivation. The Purpose Of This Work Is To Identify Possible Relationship Between N, P, K Fertilizer, Water Resource And Planting Densities. The Algorithm Used Is Multiple Regression. The Paper Focuses On The Generation Of Multiple Regression Models For The Dataset Of Sugarcane Crop For Season Adasali, Suru And Preseasonal Method. The Intercept And Slope For Variables Are Calculated And Equation For Each Model Is Generated. Sample Of N,P,K And Other Are Considered For A Period Of 7 Years From 2012 To 2018. Data Of Experimentation Is Collected For Arid Region I.E. Pandharpur, Maharashtra State.</description><identifier>ISSN: 2277-3878</identifier><identifier>EISSN: 2277-3878</identifier><identifier>DOI: 10.35940/ijrte.C4174.118419</identifier><language>eng</language><ispartof>International journal of recent technology and engineering, 2019-11, Vol.8 (4), p.1603-1606</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,781,785,27929,27930</link.rule.ids></links><search><creatorcontrib>Kale, Shivani S.</creatorcontrib><creatorcontrib>Patil, Dr. Preeti S.</creatorcontrib><creatorcontrib>Assistant professor, KIT, Kolhapur, Maharashtra, India</creatorcontrib><title>Prediction of Sugarcane Yields from Field Records using Regression Modeling</title><title>International journal of recent technology and engineering</title><description>Prediction Of Sugarcane Crop Yield Benefits The Farmer To Get Best Possible Decision Regarding Sugarcane Crop Cultivation. The Purpose Of This Work Is To Identify Possible Relationship Between N, P, K Fertilizer, Water Resource And Planting Densities. The Algorithm Used Is Multiple Regression. The Paper Focuses On The Generation Of Multiple Regression Models For The Dataset Of Sugarcane Crop For Season Adasali, Suru And Preseasonal Method. The Intercept And Slope For Variables Are Calculated And Equation For Each Model Is Generated. Sample Of N,P,K And Other Are Considered For A Period Of 7 Years From 2012 To 2018. Data Of Experimentation Is Collected For Arid Region I.E. Pandharpur, Maharashtra State.</description><issn>2277-3878</issn><issn>2277-3878</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNpNkM1OwzAQhC0EElXpE3DxCyRks5vYPqKIAqIIxM-Bk-XGdpSqjZHdHnh70pQDp50daUaaj7FrKHKsFBU3_SbuXd4QCMoBJIE6Y7OyFCJDKeT5P33JFiltiqIArIGwnrGn1-hs3-77MPDg-fuhM7E1g-NfvdvaxH0MO748av7m2hBH65D6oRu_LrqUjrnnYN129K7YhTfb5BZ_d84-l3cfzUO2erl_bG5XWQuVUllFLdS2ROOMIhKCyNcoYE3WqRJI-TUgjcOMstIgSI-yRmtUKchUlQKcMzz1tjGkFJ3X37HfmfijodATEj0h0RMSfUKCvxfIVO8</recordid><startdate>20191130</startdate><enddate>20191130</enddate><creator>Kale, Shivani S.</creator><creator>Patil, Dr. Preeti S.</creator><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>20191130</creationdate><title>Prediction of Sugarcane Yields from Field Records using Regression Modeling</title><author>Kale, Shivani S. ; Patil, Dr. Preeti S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1599-54c16d23aea9447744f6371b4de92149fb134594a9d8a318f3863da9274a55913</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><toplevel>online_resources</toplevel><creatorcontrib>Kale, Shivani S.</creatorcontrib><creatorcontrib>Patil, Dr. Preeti S.</creatorcontrib><creatorcontrib>Assistant professor, KIT, Kolhapur, Maharashtra, India</creatorcontrib><collection>CrossRef</collection><jtitle>International journal of recent technology and engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kale, Shivani S.</au><au>Patil, Dr. Preeti S.</au><aucorp>Assistant professor, KIT, Kolhapur, Maharashtra, India</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of Sugarcane Yields from Field Records using Regression Modeling</atitle><jtitle>International journal of recent technology and engineering</jtitle><date>2019-11-30</date><risdate>2019</risdate><volume>8</volume><issue>4</issue><spage>1603</spage><epage>1606</epage><pages>1603-1606</pages><issn>2277-3878</issn><eissn>2277-3878</eissn><abstract>Prediction Of Sugarcane Crop Yield Benefits The Farmer To Get Best Possible Decision Regarding Sugarcane Crop Cultivation. The Purpose Of This Work Is To Identify Possible Relationship Between N, P, K Fertilizer, Water Resource And Planting Densities. The Algorithm Used Is Multiple Regression. The Paper Focuses On The Generation Of Multiple Regression Models For The Dataset Of Sugarcane Crop For Season Adasali, Suru And Preseasonal Method. The Intercept And Slope For Variables Are Calculated And Equation For Each Model Is Generated. Sample Of N,P,K And Other Are Considered For A Period Of 7 Years From 2012 To 2018. Data Of Experimentation Is Collected For Arid Region I.E. Pandharpur, Maharashtra State.</abstract><doi>10.35940/ijrte.C4174.118419</doi><tpages>4</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2277-3878 |
| ispartof | International journal of recent technology and engineering, 2019-11, Vol.8 (4), p.1603-1606 |
| issn | 2277-3878 2277-3878 |
| language | eng |
| recordid | cdi_crossref_primary_10_35940_ijrte_C4174_118419 |
| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| title | Prediction of Sugarcane Yields from Field Records using Regression Modeling |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-15T02%3A15%3A17IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-crossref&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Prediction%20of%20Sugarcane%20Yields%20from%20Field%20Records%20using%20Regression%20Modeling&rft.jtitle=International%20journal%20of%20recent%20technology%20and%20engineering&rft.au=Kale,%20Shivani%20S.&rft.aucorp=Assistant%20professor,%20KIT,%20Kolhapur,%20Maharashtra,%20India&rft.date=2019-11-30&rft.volume=8&rft.issue=4&rft.spage=1603&rft.epage=1606&rft.pages=1603-1606&rft.issn=2277-3878&rft.eissn=2277-3878&rft_id=info:doi/10.35940/ijrte.C4174.118419&rft_dat=%3Ccrossref%3E10_35940_ijrte_C4174_118419%3C/crossref%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |