Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2262
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dc.contributor.authorWankhede, Disha S-
dc.date.accessioned2023-12-09T08:56:03Z-
dc.date.available2023-12-09T08:56:03Z-
dc.date.issued2021-
dc.identifier.citationpp. 111-121en_US
dc.identifier.isbn9783030497941-
dc.identifier.isbn9783030497958-
dc.identifier.issn2522-8595-
dc.identifier.issn2522-8609-
dc.identifier.urihttps://doi.org/10.1007/978-3-030-49795-8_10-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2262-
dc.description.abstractAgriculture is representing as an essential element in developing areas like India and other countries. The area of agriculture, computer engineering can be used for farmer decision-making for better yield and crop, also for soil quality purposes. Machine learning classifiers have a significant role in the determination of making distinct problems like soil nutrients and plant disease-related. In this paper, we have discussed the role of machine learning in the agriculture sector and also discuss distinct machine learning classifiers and their associated work in soil nutrients in the context of the agriculture sector. This paper provides a survey of various machine learning classifiers that include J48, Naive Bayes, k-NN, Random Forest, SVM, and JRip for crop prediction in the respective region according to the available soil. Most of the paper has proposed their work by considering sulfur, iron, EC, boron, OC, zinc, copper, pH, nitrogen, phosphorus, magnesium, potassium, etc., but the focus will be mostly on pH, N, P, and K. Naive Bayes classifier gives good result as related to other classifiers of machine learning used for large data set. © 2021, Springer Nature Switzerland AG.en_US
dc.language.isoenen_US
dc.publisherInternational Conference on Mobile Computing and Sustainable Informatics: ICMCSI 2020en_US
dc.subjectMachine learning classifieren_US
dc.subjectpH,N,P,Ken_US
dc.subjectSoil nutrientsen_US
dc.titleAnalysis and Prediction of Soil Nutrients Ph,N,P,K For Crop Using Machine Learning Classifier: A Reviewen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

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