Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques

dc.creatorEscorcia-Gutierrez, Jose
dc.creatorGamarra, Margarita
dc.creatorSoto-Diaz, Roosvel
dc.creatorPérez, Meglys
dc.creatorMadera, Natasha
dc.creatorMansour, Romany F.
dc.date2022-07-19T18:36:52Z
dc.date2022-07-19T18:36:52Z
dc.date2022-07-07
dc.date.accessioned2023-10-03T19:02:19Z
dc.date.available2023-10-03T19:02:19Z
dc.identifierEscorcia-Gutierrez, J.; Gamarra, M.; Soto-Diaz, R.; Pérez, M.; Madera, N.; Mansour, R.F. Intelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques. Agriculture 2022, 12, 977. https://doi.org/10.3390/agriculture12070977
dc.identifierhttps://hdl.handle.net/11323/9384
dc.identifierhttps://doi.org/10.3390/agriculture12070977
dc.identifier10.3390/agriculture12070977
dc.identifier2077-0472
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9167011
dc.descriptionSoil nutrients are a vital part of soil fertility and other environmental factors. Soil testing is an efficient tool used to evaluate the existing nutrient levels of soil and aid to compute the appropriate quantity of soil nutrients depending upon the fertility level and crop requirements. Since the conventional soil nutrient testing models are not feasible in real time applications, an efficient soil nutrient, and potential of hydrogen (pH) prediction models are essential to improve overall crop productivity. In this aspect, this paper aims to design an intelligent soil nutrient and pH classification using weighted voting ensemble deep learning (ISNpHC-WVE) technique. The proposed ISNpHC-WVE technique aims to classify the existence of nutrients and pH levels exist in the soil. In addition, three deep learning (DL) models namely gated recurrent unit (GRU), deep belief network (DBN), and bidirectional long short term memory (BiLSTM) were used for the predictive analysis. Moreover, a weighted voting ensemble model was employed which allows a weight vector on every DL model of the ensemble depending upon the attained accuracy on every class. Furthermore, the hyperparameter optimization of the three DL models was performed using manta ray foraging optimization (MRFO) algorithm. For investigating the enhanced predictive performance of the ISNpHC-WVE technique, a comprehensive simulation analysis takes place to examine the pH and soil nutrient classification performance. The experimental results showcased the better performance of the ISNpHC-WVE technique over the recent techniques with accuracy of 0.9281 and 0.9497 on soil nutrient and soil pH classification. The proposed model can be utilized as an effective tool to improve productivity in agriculture by proper soil nutrient and pH classification.
dc.format16 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.publisherSwitzerland
dc.relationAgriculture (Switzerland)
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dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourcehttps://www.mdpi.com/2077-0472/12/7/977
dc.subjectSoil nutrients
dc.subjectpH classification
dc.subjectAgriculture
dc.subjectSoil management
dc.subjectDeep learning
dc.subjectEnsemble model
dc.titleIntelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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