Voting Classifier-Based Crop Recommendation

Abstract The three most important necessities for human life are food, shelter, and clothing. Young people who are technologically savvy have witnessed a significant scientific increase in the latter two areas. Despite this, farming is still regarded as a labor-intensive endeavour. Most farmers are...
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Autor*in:	Bandi, Raswitha [verfasserIn] Likhit, M. Sai Surya Reddy, S. Rajavardhan Bodla, Sathwik Raj Venkat, Vempati Sai

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Crop recommendation Machine learning Ensemble model Voting classifier Recommendation system

Anmerkung:	© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: SN Computer Science - Singapore : Springer Singapore, 2020, 4(2023), 5 vom: 06. Juli
Übergeordnetes Werk:	volume:4 ; year:2023 ; number:5 ; day:06 ; month:07

Links:	Volltext

DOI / URN:	10.1007/s42979-023-01995-8

Katalog-ID:	SPR052176681

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520			\|a Abstract The three most important necessities for human life are food, shelter, and clothing. Young people who are technologically savvy have witnessed a significant scientific increase in the latter two areas. Despite this, farming is still regarded as a labor-intensive endeavour. Most farmers are uneducated and lack a scientific understanding of farming practices. Crop cultivation anywhere in the world is dependent on the climate, also known as seasons, and soil properties; however, increasing crop production is dependent on a variety of factors, most notably temperature. This work proposes a crop recommendation system to address the issue of increasing crop production. A vision of the perfect harvest before planting would be extremely beneficial to farmers and other stakeholders in making appropriate decisions about improving yields for local use, and it may inspire increased capacity and a wider range of product options for businesses. Precision agriculture is a modern farming strategy that advises farmers on the sorts of crops they should plant based on data collected through studies on soil features, soil types, and crop yields. This style of agriculture is also known as "high-intensity agriculture". Our system employed Machine Learning procedures to recommend the appropriate crops. This system then reduces the financial losses experienced by farmers because of establishing the ominous harvests. This problem is addressed in this paper by proposing a recommendation system using an ensemble model with majority voting and an accuracy of 99.4 percent.
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allfields	10.1007/s42979-023-01995-8 doi (DE-627)SPR052176681 (SPR)s42979-023-01995-8-e DE-627 ger DE-627 rakwb eng Bandi, Raswitha verfasserin (orcid)0000-0002-3916-4374 aut Voting Classifier-Based Crop Recommendation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The three most important necessities for human life are food, shelter, and clothing. Young people who are technologically savvy have witnessed a significant scientific increase in the latter two areas. Despite this, farming is still regarded as a labor-intensive endeavour. Most farmers are uneducated and lack a scientific understanding of farming practices. Crop cultivation anywhere in the world is dependent on the climate, also known as seasons, and soil properties; however, increasing crop production is dependent on a variety of factors, most notably temperature. This work proposes a crop recommendation system to address the issue of increasing crop production. A vision of the perfect harvest before planting would be extremely beneficial to farmers and other stakeholders in making appropriate decisions about improving yields for local use, and it may inspire increased capacity and a wider range of product options for businesses. Precision agriculture is a modern farming strategy that advises farmers on the sorts of crops they should plant based on data collected through studies on soil features, soil types, and crop yields. This style of agriculture is also known as "high-intensity agriculture". Our system employed Machine Learning procedures to recommend the appropriate crops. This system then reduces the financial losses experienced by farmers because of establishing the ominous harvests. This problem is addressed in this paper by proposing a recommendation system using an ensemble model with majority voting and an accuracy of 99.4 percent. Crop recommendation (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ensemble model (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Likhit, M. Sai Surya aut Reddy, S. Rajavardhan aut Bodla, Sathwik Raj aut Venkat, Vempati Sai aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 4(2023), 5 vom: 06. Juli (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:4 year:2023 number:5 day:06 month:07 https://dx.doi.org/10.1007/s42979-023-01995-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2023 5 06 07
spelling	10.1007/s42979-023-01995-8 doi (DE-627)SPR052176681 (SPR)s42979-023-01995-8-e DE-627 ger DE-627 rakwb eng Bandi, Raswitha verfasserin (orcid)0000-0002-3916-4374 aut Voting Classifier-Based Crop Recommendation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The three most important necessities for human life are food, shelter, and clothing. Young people who are technologically savvy have witnessed a significant scientific increase in the latter two areas. Despite this, farming is still regarded as a labor-intensive endeavour. Most farmers are uneducated and lack a scientific understanding of farming practices. Crop cultivation anywhere in the world is dependent on the climate, also known as seasons, and soil properties; however, increasing crop production is dependent on a variety of factors, most notably temperature. This work proposes a crop recommendation system to address the issue of increasing crop production. A vision of the perfect harvest before planting would be extremely beneficial to farmers and other stakeholders in making appropriate decisions about improving yields for local use, and it may inspire increased capacity and a wider range of product options for businesses. Precision agriculture is a modern farming strategy that advises farmers on the sorts of crops they should plant based on data collected through studies on soil features, soil types, and crop yields. This style of agriculture is also known as "high-intensity agriculture". Our system employed Machine Learning procedures to recommend the appropriate crops. This system then reduces the financial losses experienced by farmers because of establishing the ominous harvests. This problem is addressed in this paper by proposing a recommendation system using an ensemble model with majority voting and an accuracy of 99.4 percent. Crop recommendation (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ensemble model (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Likhit, M. Sai Surya aut Reddy, S. Rajavardhan aut Bodla, Sathwik Raj aut Venkat, Vempati Sai aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 4(2023), 5 vom: 06. Juli (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:4 year:2023 number:5 day:06 month:07 https://dx.doi.org/10.1007/s42979-023-01995-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2023 5 06 07
allfields_unstemmed	10.1007/s42979-023-01995-8 doi (DE-627)SPR052176681 (SPR)s42979-023-01995-8-e DE-627 ger DE-627 rakwb eng Bandi, Raswitha verfasserin (orcid)0000-0002-3916-4374 aut Voting Classifier-Based Crop Recommendation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The three most important necessities for human life are food, shelter, and clothing. Young people who are technologically savvy have witnessed a significant scientific increase in the latter two areas. Despite this, farming is still regarded as a labor-intensive endeavour. Most farmers are uneducated and lack a scientific understanding of farming practices. Crop cultivation anywhere in the world is dependent on the climate, also known as seasons, and soil properties; however, increasing crop production is dependent on a variety of factors, most notably temperature. This work proposes a crop recommendation system to address the issue of increasing crop production. A vision of the perfect harvest before planting would be extremely beneficial to farmers and other stakeholders in making appropriate decisions about improving yields for local use, and it may inspire increased capacity and a wider range of product options for businesses. Precision agriculture is a modern farming strategy that advises farmers on the sorts of crops they should plant based on data collected through studies on soil features, soil types, and crop yields. This style of agriculture is also known as "high-intensity agriculture". Our system employed Machine Learning procedures to recommend the appropriate crops. This system then reduces the financial losses experienced by farmers because of establishing the ominous harvests. This problem is addressed in this paper by proposing a recommendation system using an ensemble model with majority voting and an accuracy of 99.4 percent. Crop recommendation (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ensemble model (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Likhit, M. Sai Surya aut Reddy, S. Rajavardhan aut Bodla, Sathwik Raj aut Venkat, Vempati Sai aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 4(2023), 5 vom: 06. Juli (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:4 year:2023 number:5 day:06 month:07 https://dx.doi.org/10.1007/s42979-023-01995-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2023 5 06 07
allfieldsGer	10.1007/s42979-023-01995-8 doi (DE-627)SPR052176681 (SPR)s42979-023-01995-8-e DE-627 ger DE-627 rakwb eng Bandi, Raswitha verfasserin (orcid)0000-0002-3916-4374 aut Voting Classifier-Based Crop Recommendation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The three most important necessities for human life are food, shelter, and clothing. Young people who are technologically savvy have witnessed a significant scientific increase in the latter two areas. Despite this, farming is still regarded as a labor-intensive endeavour. Most farmers are uneducated and lack a scientific understanding of farming practices. Crop cultivation anywhere in the world is dependent on the climate, also known as seasons, and soil properties; however, increasing crop production is dependent on a variety of factors, most notably temperature. This work proposes a crop recommendation system to address the issue of increasing crop production. A vision of the perfect harvest before planting would be extremely beneficial to farmers and other stakeholders in making appropriate decisions about improving yields for local use, and it may inspire increased capacity and a wider range of product options for businesses. Precision agriculture is a modern farming strategy that advises farmers on the sorts of crops they should plant based on data collected through studies on soil features, soil types, and crop yields. This style of agriculture is also known as "high-intensity agriculture". Our system employed Machine Learning procedures to recommend the appropriate crops. This system then reduces the financial losses experienced by farmers because of establishing the ominous harvests. This problem is addressed in this paper by proposing a recommendation system using an ensemble model with majority voting and an accuracy of 99.4 percent. Crop recommendation (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ensemble model (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Likhit, M. Sai Surya aut Reddy, S. Rajavardhan aut Bodla, Sathwik Raj aut Venkat, Vempati Sai aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 4(2023), 5 vom: 06. Juli (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:4 year:2023 number:5 day:06 month:07 https://dx.doi.org/10.1007/s42979-023-01995-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2023 5 06 07
allfieldsSound	10.1007/s42979-023-01995-8 doi (DE-627)SPR052176681 (SPR)s42979-023-01995-8-e DE-627 ger DE-627 rakwb eng Bandi, Raswitha verfasserin (orcid)0000-0002-3916-4374 aut Voting Classifier-Based Crop Recommendation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract The three most important necessities for human life are food, shelter, and clothing. Young people who are technologically savvy have witnessed a significant scientific increase in the latter two areas. Despite this, farming is still regarded as a labor-intensive endeavour. Most farmers are uneducated and lack a scientific understanding of farming practices. Crop cultivation anywhere in the world is dependent on the climate, also known as seasons, and soil properties; however, increasing crop production is dependent on a variety of factors, most notably temperature. This work proposes a crop recommendation system to address the issue of increasing crop production. A vision of the perfect harvest before planting would be extremely beneficial to farmers and other stakeholders in making appropriate decisions about improving yields for local use, and it may inspire increased capacity and a wider range of product options for businesses. Precision agriculture is a modern farming strategy that advises farmers on the sorts of crops they should plant based on data collected through studies on soil features, soil types, and crop yields. This style of agriculture is also known as "high-intensity agriculture". Our system employed Machine Learning procedures to recommend the appropriate crops. This system then reduces the financial losses experienced by farmers because of establishing the ominous harvests. This problem is addressed in this paper by proposing a recommendation system using an ensemble model with majority voting and an accuracy of 99.4 percent. Crop recommendation (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ensemble model (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213 Likhit, M. Sai Surya aut Reddy, S. Rajavardhan aut Bodla, Sathwik Raj aut Venkat, Vempati Sai aut Enthalten in SN Computer Science Singapore : Springer Singapore, 2020 4(2023), 5 vom: 06. Juli (DE-627)1668832976 (DE-600)2977367-2 2661-8907 nnns volume:4 year:2023 number:5 day:06 month:07 https://dx.doi.org/10.1007/s42979-023-01995-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2023 5 06 07
language	English
source	Enthalten in SN Computer Science 4(2023), 5 vom: 06. Juli volume:4 year:2023 number:5 day:06 month:07
sourceStr	Enthalten in SN Computer Science 4(2023), 5 vom: 06. Juli volume:4 year:2023 number:5 day:06 month:07
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Crop recommendation Machine learning Ensemble model Voting classifier Recommendation system
isfreeaccess_bool	false
container_title	SN Computer Science
authorswithroles_txt_mv	Bandi, Raswitha @@aut@@ Likhit, M. Sai Surya @@aut@@ Reddy, S. Rajavardhan @@aut@@ Bodla, Sathwik Raj @@aut@@ Venkat, Vempati Sai @@aut@@
publishDateDaySort_date	2023-07-06T00:00:00Z
hierarchy_top_id	1668832976
id	SPR052176681
language_de	englisch
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author	Bandi, Raswitha
spellingShingle	Bandi, Raswitha misc Crop recommendation misc Machine learning misc Ensemble model misc Voting classifier misc Recommendation system Voting Classifier-Based Crop Recommendation
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topic_title	Voting Classifier-Based Crop Recommendation Crop recommendation (dpeaa)DE-He213 Machine learning (dpeaa)DE-He213 Ensemble model (dpeaa)DE-He213 Voting classifier (dpeaa)DE-He213 Recommendation system (dpeaa)DE-He213
topic	misc Crop recommendation misc Machine learning misc Ensemble model misc Voting classifier misc Recommendation system
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title	Voting Classifier-Based Crop Recommendation
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title_sort	voting classifier-based crop recommendation
title_auth	Voting Classifier-Based Crop Recommendation
abstract	Abstract The three most important necessities for human life are food, shelter, and clothing. Young people who are technologically savvy have witnessed a significant scientific increase in the latter two areas. Despite this, farming is still regarded as a labor-intensive endeavour. Most farmers are uneducated and lack a scientific understanding of farming practices. Crop cultivation anywhere in the world is dependent on the climate, also known as seasons, and soil properties; however, increasing crop production is dependent on a variety of factors, most notably temperature. This work proposes a crop recommendation system to address the issue of increasing crop production. A vision of the perfect harvest before planting would be extremely beneficial to farmers and other stakeholders in making appropriate decisions about improving yields for local use, and it may inspire increased capacity and a wider range of product options for businesses. Precision agriculture is a modern farming strategy that advises farmers on the sorts of crops they should plant based on data collected through studies on soil features, soil types, and crop yields. This style of agriculture is also known as "high-intensity agriculture". Our system employed Machine Learning procedures to recommend the appropriate crops. This system then reduces the financial losses experienced by farmers because of establishing the ominous harvests. This problem is addressed in this paper by proposing a recommendation system using an ensemble model with majority voting and an accuracy of 99.4 percent. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract The three most important necessities for human life are food, shelter, and clothing. Young people who are technologically savvy have witnessed a significant scientific increase in the latter two areas. Despite this, farming is still regarded as a labor-intensive endeavour. Most farmers are uneducated and lack a scientific understanding of farming practices. Crop cultivation anywhere in the world is dependent on the climate, also known as seasons, and soil properties; however, increasing crop production is dependent on a variety of factors, most notably temperature. This work proposes a crop recommendation system to address the issue of increasing crop production. A vision of the perfect harvest before planting would be extremely beneficial to farmers and other stakeholders in making appropriate decisions about improving yields for local use, and it may inspire increased capacity and a wider range of product options for businesses. Precision agriculture is a modern farming strategy that advises farmers on the sorts of crops they should plant based on data collected through studies on soil features, soil types, and crop yields. This style of agriculture is also known as "high-intensity agriculture". Our system employed Machine Learning procedures to recommend the appropriate crops. This system then reduces the financial losses experienced by farmers because of establishing the ominous harvests. This problem is addressed in this paper by proposing a recommendation system using an ensemble model with majority voting and an accuracy of 99.4 percent. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract The three most important necessities for human life are food, shelter, and clothing. Young people who are technologically savvy have witnessed a significant scientific increase in the latter two areas. Despite this, farming is still regarded as a labor-intensive endeavour. Most farmers are uneducated and lack a scientific understanding of farming practices. Crop cultivation anywhere in the world is dependent on the climate, also known as seasons, and soil properties; however, increasing crop production is dependent on a variety of factors, most notably temperature. This work proposes a crop recommendation system to address the issue of increasing crop production. A vision of the perfect harvest before planting would be extremely beneficial to farmers and other stakeholders in making appropriate decisions about improving yields for local use, and it may inspire increased capacity and a wider range of product options for businesses. Precision agriculture is a modern farming strategy that advises farmers on the sorts of crops they should plant based on data collected through studies on soil features, soil types, and crop yields. This style of agriculture is also known as "high-intensity agriculture". Our system employed Machine Learning procedures to recommend the appropriate crops. This system then reduces the financial losses experienced by farmers because of establishing the ominous harvests. This problem is addressed in this paper by proposing a recommendation system using an ensemble model with majority voting and an accuracy of 99.4 percent. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Voting Classifier-Based Crop Recommendation
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author2	Likhit, M. Sai Surya Reddy, S. Rajavardhan Bodla, Sathwik Raj Venkat, Vempati Sai
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up_date	2024-07-04T01:38:58.469Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR052176681</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230707064811.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230707s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s42979-023-01995-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR052176681</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s42979-023-01995-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bandi, Raswitha</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-3916-4374</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Voting Classifier-Based Crop Recommendation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The three most important necessities for human life are food, shelter, and clothing. Young people who are technologically savvy have witnessed a significant scientific increase in the latter two areas. Despite this, farming is still regarded as a labor-intensive endeavour. Most farmers are uneducated and lack a scientific understanding of farming practices. Crop cultivation anywhere in the world is dependent on the climate, also known as seasons, and soil properties; however, increasing crop production is dependent on a variety of factors, most notably temperature. This work proposes a crop recommendation system to address the issue of increasing crop production. A vision of the perfect harvest before planting would be extremely beneficial to farmers and other stakeholders in making appropriate decisions about improving yields for local use, and it may inspire increased capacity and a wider range of product options for businesses. Precision agriculture is a modern farming strategy that advises farmers on the sorts of crops they should plant based on data collected through studies on soil features, soil types, and crop yields. This style of agriculture is also known as "high-intensity agriculture". Our system employed Machine Learning procedures to recommend the appropriate crops. This system then reduces the financial losses experienced by farmers because of establishing the ominous harvests. This problem is addressed in this paper by proposing a recommendation system using an ensemble model with majority voting and an accuracy of 99.4 percent.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Crop recommendation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ensemble model</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Voting classifier</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Recommendation system</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Likhit, M. Sai Surya</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Reddy, S. Rajavardhan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bodla, Sathwik Raj</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Venkat, Vempati Sai</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">SN Computer Science</subfield><subfield code="d">Singapore : Springer Singapore, 2020</subfield><subfield code="g">4(2023), 5 vom: 06. Juli</subfield><subfield code="w">(DE-627)1668832976</subfield><subfield code="w">(DE-600)2977367-2</subfield><subfield code="x">2661-8907</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:4</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:5</subfield><subfield code="g">day:06</subfield><subfield code="g">month:07</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s42979-023-01995-8</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield 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