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Soil NPK Prediction using Enhanced Genetic Algorithm
Agriculture is one of the primary sources of livelihood and revenue in the globe that satisfies human needs. To gain healthy crop yields, the farmers should give importance to the soil health. The objective of this work is to propose an Enhanced Genetic Algorithm (EGA) model for soil nutrient predic...
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| container_end_page | 2018 |
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| container_start_page | 2014 |
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| creator | Irene Monica, N Pooja, Shree R Rithiga, S Madhumathi, R |
| description | Agriculture is one of the primary sources of livelihood and revenue in the globe that satisfies human needs. To gain healthy crop yields, the farmers should give importance to the soil health. The objective of this work is to propose an Enhanced Genetic Algorithm (EGA) model for soil nutrient prediction using the factors like temperature, humidity, pH, and rainfall. The algorithm calculates the Nitrogen, Phosphorus, and Potassium (NPK) values based on these inputs and uses rank-based selection for fitness selection. Enhanced crossover and mutation techniques are also done to increase its performance. In order to accurately identify the NPK values in soil, the enhanced genetic algorithm has been used. The algorithm's accuracy is evaluated using a dataset of soil, and it is found to be higher than that of conventional genetic algorithm. By providing more detailed and timely soil nutrient data, this enhanced genetic algorithm has the potential to help farmers optimize their crop yield. |
| doi_str_mv | 10.1109/ICACCS57279.2023.10113121 |
| format | conference_proceeding |
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To gain healthy crop yields, the farmers should give importance to the soil health. The objective of this work is to propose an Enhanced Genetic Algorithm (EGA) model for soil nutrient prediction using the factors like temperature, humidity, pH, and rainfall. The algorithm calculates the Nitrogen, Phosphorus, and Potassium (NPK) values based on these inputs and uses rank-based selection for fitness selection. Enhanced crossover and mutation techniques are also done to increase its performance. In order to accurately identify the NPK values in soil, the enhanced genetic algorithm has been used. The algorithm's accuracy is evaluated using a dataset of soil, and it is found to be higher than that of conventional genetic algorithm. By providing more detailed and timely soil nutrient data, this enhanced genetic algorithm has the potential to help farmers optimize their crop yield.</description><identifier>EISSN: 2575-7288</identifier><identifier>EISBN: 9798350397376</identifier><identifier>DOI: 10.1109/ICACCS57279.2023.10113121</identifier><language>eng</language><publisher>IEEE</publisher><subject>Agriculture ; Crops ; Genetic Algorithm ; Humidity ; NPK ; Phosphorus ; Prediction algorithms ; Predictive models ; Rank-based Selection ; Soil ; Soil Nutrient Prediction ; Temperature ; Two-point Crossover</subject><ispartof>2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), 2023, Vol.1, p.2014-2018</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10113121$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,27902,54530,54907</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10113121$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Irene Monica, N</creatorcontrib><creatorcontrib>Pooja, Shree R</creatorcontrib><creatorcontrib>Rithiga, S</creatorcontrib><creatorcontrib>Madhumathi, R</creatorcontrib><title>Soil NPK Prediction using Enhanced Genetic Algorithm</title><title>2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)</title><addtitle>ICACCS</addtitle><description>Agriculture is one of the primary sources of livelihood and revenue in the globe that satisfies human needs. To gain healthy crop yields, the farmers should give importance to the soil health. The objective of this work is to propose an Enhanced Genetic Algorithm (EGA) model for soil nutrient prediction using the factors like temperature, humidity, pH, and rainfall. The algorithm calculates the Nitrogen, Phosphorus, and Potassium (NPK) values based on these inputs and uses rank-based selection for fitness selection. Enhanced crossover and mutation techniques are also done to increase its performance. In order to accurately identify the NPK values in soil, the enhanced genetic algorithm has been used. The algorithm's accuracy is evaluated using a dataset of soil, and it is found to be higher than that of conventional genetic algorithm. By providing more detailed and timely soil nutrient data, this enhanced genetic algorithm has the potential to help farmers optimize their crop yield.</description><subject>Agriculture</subject><subject>Crops</subject><subject>Genetic Algorithm</subject><subject>Humidity</subject><subject>NPK</subject><subject>Phosphorus</subject><subject>Prediction algorithms</subject><subject>Predictive models</subject><subject>Rank-based Selection</subject><subject>Soil</subject><subject>Soil Nutrient Prediction</subject><subject>Temperature</subject><subject>Two-point Crossover</subject><issn>2575-7288</issn><isbn>9798350397376</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2023</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1j0FOwzAQRQ0SElXJDViYA6TMeOrYXlZRKYgKKhXWVWKPW6M0QUlYcHsiAau_eXp6X4g7hAUiuPunclWWe22UcQsFihYIiIQKL0TmjLOkgZwhU1yKmdJG50ZZey2yYfgAAELrrLUzsdx3qZEvu2e56zkkP6aulV9Dao9y3Z6q1nOQG255TF6ummPXp_F0vhFXsWoGzv52Lt4f1m_lY7593Uxd2zwhujEPXLnIwLW1EG1Rm6nOR--Cd4U2rImgAA--IkRDRTQ16QlaBqijUxhoLm5_vYmZD599Olf99-H_KP0ASp9HEQ</recordid><startdate>20230317</startdate><enddate>20230317</enddate><creator>Irene Monica, N</creator><creator>Pooja, Shree R</creator><creator>Rithiga, S</creator><creator>Madhumathi, R</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20230317</creationdate><title>Soil NPK Prediction using Enhanced Genetic Algorithm</title><author>Irene Monica, N ; Pooja, Shree R ; Rithiga, S ; Madhumathi, R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i119t-dea9fe0eb880f86b7121cfc9dc9657e533060c0ca311736f7b35b714d0bf921d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Agriculture</topic><topic>Crops</topic><topic>Genetic Algorithm</topic><topic>Humidity</topic><topic>NPK</topic><topic>Phosphorus</topic><topic>Prediction algorithms</topic><topic>Predictive models</topic><topic>Rank-based Selection</topic><topic>Soil</topic><topic>Soil Nutrient Prediction</topic><topic>Temperature</topic><topic>Two-point Crossover</topic><toplevel>online_resources</toplevel><creatorcontrib>Irene Monica, N</creatorcontrib><creatorcontrib>Pooja, Shree R</creatorcontrib><creatorcontrib>Rithiga, S</creatorcontrib><creatorcontrib>Madhumathi, R</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Irene Monica, N</au><au>Pooja, Shree R</au><au>Rithiga, S</au><au>Madhumathi, R</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Soil NPK Prediction using Enhanced Genetic Algorithm</atitle><btitle>2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS)</btitle><stitle>ICACCS</stitle><date>2023-03-17</date><risdate>2023</risdate><volume>1</volume><spage>2014</spage><epage>2018</epage><pages>2014-2018</pages><eissn>2575-7288</eissn><eisbn>9798350397376</eisbn><abstract>Agriculture is one of the primary sources of livelihood and revenue in the globe that satisfies human needs. To gain healthy crop yields, the farmers should give importance to the soil health. The objective of this work is to propose an Enhanced Genetic Algorithm (EGA) model for soil nutrient prediction using the factors like temperature, humidity, pH, and rainfall. The algorithm calculates the Nitrogen, Phosphorus, and Potassium (NPK) values based on these inputs and uses rank-based selection for fitness selection. Enhanced crossover and mutation techniques are also done to increase its performance. In order to accurately identify the NPK values in soil, the enhanced genetic algorithm has been used. The algorithm's accuracy is evaluated using a dataset of soil, and it is found to be higher than that of conventional genetic algorithm. By providing more detailed and timely soil nutrient data, this enhanced genetic algorithm has the potential to help farmers optimize their crop yield.</abstract><pub>IEEE</pub><doi>10.1109/ICACCS57279.2023.10113121</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2575-7288 |
| ispartof | 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS), 2023, Vol.1, p.2014-2018 |
| issn | 2575-7288 |
| language | eng |
| recordid | cdi_ieee_primary_10113121 |
| source | IEEE Xplore All Conference Series |
| subjects | Agriculture Crops Genetic Algorithm Humidity NPK Phosphorus Prediction algorithms Predictive models Rank-based Selection Soil Soil Nutrient Prediction Temperature Two-point Crossover |
| title | Soil NPK Prediction using Enhanced Genetic Algorithm |
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