Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques
Soil 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...
- Autores:
-
Escorcia-Gutierrez, Jose
Gamarra, Margarita
Soto-Diaz, Roosvel
Pérez, Meglys
Madera, Natasha
Mansour, Romany F.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9384
- Acceso en línea:
- https://hdl.handle.net/11323/9384
https://doi.org/10.3390/agriculture12070977
https://repositorio.cuc.edu.co/
- Palabra clave:
- Soil nutrients
pH classification
Agriculture
Soil management
Deep learning
Ensemble model
- Rights
- openAccess
- License
- © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
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dc.title.eng.fl_str_mv |
Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques |
title |
Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques |
spellingShingle |
Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques Soil nutrients pH classification Agriculture Soil management Deep learning Ensemble model |
title_short |
Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques |
title_full |
Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques |
title_fullStr |
Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques |
title_full_unstemmed |
Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques |
title_sort |
Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniques |
dc.creator.fl_str_mv |
Escorcia-Gutierrez, Jose Gamarra, Margarita Soto-Diaz, Roosvel Pérez, Meglys Madera, Natasha Mansour, Romany F. |
dc.contributor.author.spa.fl_str_mv |
Escorcia-Gutierrez, Jose Gamarra, Margarita Soto-Diaz, Roosvel Pérez, Meglys Madera, Natasha Mansour, Romany F. |
dc.subject.proposal.eng.fl_str_mv |
Soil nutrients pH classification Agriculture Soil management Deep learning Ensemble model |
topic |
Soil nutrients pH classification Agriculture Soil management Deep learning Ensemble model |
description |
Soil 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. |
publishDate |
2022 |
dc.date.accessioned.none.fl_str_mv |
2022-07-19T18:36:52Z |
dc.date.available.none.fl_str_mv |
2022-07-19T18:36:52Z |
dc.date.issued.none.fl_str_mv |
2022-07-07 |
dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Escorcia-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.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/9384 |
dc.identifier.url.spa.fl_str_mv |
https://doi.org/10.3390/agriculture12070977 |
dc.identifier.doi.spa.fl_str_mv |
10.3390/agriculture12070977 |
dc.identifier.eissn.spa.fl_str_mv |
2077-0472 |
dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
identifier_str_mv |
Escorcia-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 10.3390/agriculture12070977 2077-0472 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
url |
https://hdl.handle.net/11323/9384 https://doi.org/10.3390/agriculture12070977 https://repositorio.cuc.edu.co/ |
dc.language.iso.none.fl_str_mv |
eng |
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eng |
dc.relation.ispartofjournal.spa.fl_str_mv |
Agriculture (Switzerland) |
dc.relation.references.spa.fl_str_mv |
1. Patel, H.; Patel, D. A brief survey of data mining techniques applied to agricultural data. Int. J. Comput. Appl. 2014, 95, 80–83. [CrossRef] 2. Padarian, J.; Minasny, B.; McBratney, A.B. Using deep learning to predict soil properties from regional spectral data. Geoderma Reg. 2019, 16, e00198. [CrossRef] 3. Ji, C.; Liu, H.; Cha, Z.; Lin, Q.; Feng, G. Spatial-Temporal Variation of N, P, and K Stoichiometry in Cropland of Hainan Island. Agriculture 2021, 12, 39. [CrossRef] 4. Kayad, A.; Sozzi, M.; Gatto, S.; Whelan, B.; Sartori, L.; Marinello, F. Ten years of corn yield dynamics at field scale under digital agriculture solutions: A case study from North Italy. Comput. Electron. Agric. 2021, 185, 106126. [CrossRef] 5. Taghizadeh-Mehrjardi, R.; Khademi, H.; Khayamim, F.; Zeraatpisheh, M.; Heung, B.; Scholten, T. A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties. Remote Sens. 2022, 14, 472. [CrossRef] 6. Zeraatpisheh, M.; Garosi, Y.; Owliaie, H.R.; Ayoubi, S.; Taghizadeh-Mehrjardi, R.; Scholten, T.; Xu, M. Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates. Catena 2022, 208, 105723. [CrossRef] 7. Davenport, J.; Jabro, J. Assessment of hand held ion selective electrode technology for direct measurement of soil chemical properties. Commun. Soil Sci. Plant Anal. 2011, 32, 3077–3085. [CrossRef] 8. Yang, M.; Xu, D.; Chen, S.; Li, H.; Shi, Z. Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra. Sensors 2019, 19, 263. [CrossRef] 9. Yu, H.; Liu, D.; Chen, G.; Wan, B.; Wang, S.; Yang, B. A neural network ensemble method for precision fertilization modeling. Math. Comput. Model. 2010, 51, 1375–1382. [CrossRef] 10. Suchithra, M.S.; Pai, M.L. Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters. Inf. Process. Agric. 2020, 7, 72–82. [CrossRef] 11. Chambers, O. Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method. Sensors 2021, 21, 4208. 12. Wu, C.; Chen, Y.; Hong, X.; Liu, Z.; Peng, C. Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques. For. Ecosyst. 2020, 7, 30. [CrossRef] 13. Rose, S.; Nickolas, S.; Sangeetha, S. Machine Learning and Statistical Approaches used in Estimating Parameters that Affect the Soil Fertility Status: A Survey. In Proceedings of the 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), Karnataka, India, 16–18 August 2018; IEEE: New York, NY, USA, 2018; pp. 381–385. 14. Rajamanickam, J. Predictive model construction for prediction of soil fertility using decision tree machine learning algorithm. INFOCOMP J. Comput. Sci. 2021, 20, 49–55. 15. Rajamanickam, J.; Mani, S.D. Kullback chi square and Gustafson Kessel probabilistic neural network based soil fertility prediction. Concurr. Comput. Pract. Exp. 2021, 33, e6460. [CrossRef] 16. Sirsat, M.S.; Cernadas, E.; Fernández-Delgado, M.; Barro, S. Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods. Comput. Electron. Agric. 2018, 154, 120–133. [CrossRef] 17. Ning, J.; Sheng, M.; Yi, X.; Wang, Y.; Hou, Z.; Zhang, Z.; Gu, X. Rapid evaluation of soil fertility in tea plantation based on near-infrared spectroscopy. Spectrosc. Lett. 2018, 51, 463–471. [CrossRef] 18. Wang, J.; Wang, Y.; Yang, J. Forecasting of Significant Wave Height Based on Gated Recurrent Unit Network in the Taiwan Straitand Its Adjacent Waters. Water 2021, 13, 86. [CrossRef] 19. Hinton, G.E. Deep belief network. Scholarpedia 2009, 4, 5947. [CrossRef] 20. Sokkhey, P.; Okazaki, T. Development and Optimization of Deep Belief Networks Applied for Academic Performance Prediction with Larger Datasets. IEIE Trans. Smart Process. Comput. 2020, 9, 298–311. [CrossRef] 21. Minh-Tuan, N.; Kim, Y.H. Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems. Appl. Sci. 2019, 9, 3470. [CrossRef] 22. Hemeida, M.G.; Ibrahim, A.A.; Mohamed, A.A.A.; Alkhalaf, S.; El-Dine, A.M.B. Optimal allocation of distributed generators DG based Manta Ray Foraging Optimization algorithm (MRFO). Ain Shams Eng. J. 2021, 12, 609–619. [CrossRef] 23. Livieris, I.E.; Kanavos, A.; Tampakas, V.; Pintelas, P. A weighted voting ensemble self-labeled algorithm for the detection of lung abnormalities from X-rays. Algorithms 2019, 12, 64. [CrossRef] |
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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. Atribución 4.0 Internacional (CC BY 4.0) |
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https://creativecommons.org/licenses/by/4.0/ |
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Escorcia-Gutierrez, JoseGamarra, MargaritaSoto-Diaz, RoosvelPérez, MeglysMadera, NatashaMansour, Romany F.2022-07-19T18:36:52Z2022-07-19T18:36:52Z2022-07-07Escorcia-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/agriculture12070977https://hdl.handle.net/11323/9384https://doi.org/10.3390/agriculture1207097710.3390/agriculture120709772077-0472Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Soil 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.16 páginasapplication/pdfengMultidisciplinary Digital Publishing Institute (MDPI)Switzerland© 2022 by the authors. Licensee MDPI, Basel, Switzerland.Atribución 4.0 Internacional (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Intelligent agricultural modelling of soil nutrients and ph classification using ensemble deep learning techniquesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85https://www.mdpi.com/2077-0472/12/7/977Agriculture (Switzerland)1. Patel, H.; Patel, D. A brief survey of data mining techniques applied to agricultural data. Int. J. Comput. Appl. 2014, 95, 80–83. [CrossRef]2. Padarian, J.; Minasny, B.; McBratney, A.B. Using deep learning to predict soil properties from regional spectral data. Geoderma Reg. 2019, 16, e00198. [CrossRef]3. Ji, C.; Liu, H.; Cha, Z.; Lin, Q.; Feng, G. Spatial-Temporal Variation of N, P, and K Stoichiometry in Cropland of Hainan Island. Agriculture 2021, 12, 39. [CrossRef]4. Kayad, A.; Sozzi, M.; Gatto, S.; Whelan, B.; Sartori, L.; Marinello, F. Ten years of corn yield dynamics at field scale under digital agriculture solutions: A case study from North Italy. Comput. Electron. Agric. 2021, 185, 106126. [CrossRef]5. Taghizadeh-Mehrjardi, R.; Khademi, H.; Khayamim, F.; Zeraatpisheh, M.; Heung, B.; Scholten, T. A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties. Remote Sens. 2022, 14, 472. [CrossRef]6. Zeraatpisheh, M.; Garosi, Y.; Owliaie, H.R.; Ayoubi, S.; Taghizadeh-Mehrjardi, R.; Scholten, T.; Xu, M. Improving the spatial prediction of soil organic carbon using environmental covariates selection: A comparison of a group of environmental covariates. Catena 2022, 208, 105723. [CrossRef]7. Davenport, J.; Jabro, J. Assessment of hand held ion selective electrode technology for direct measurement of soil chemical properties. Commun. Soil Sci. Plant Anal. 2011, 32, 3077–3085. [CrossRef]8. Yang, M.; Xu, D.; Chen, S.; Li, H.; Shi, Z. Evaluation of Machine Learning Approaches to Predict Soil Organic Matter and pH Using vis-NIR Spectra. Sensors 2019, 19, 263. [CrossRef]9. Yu, H.; Liu, D.; Chen, G.; Wan, B.; Wang, S.; Yang, B. A neural network ensemble method for precision fertilization modeling. Math. Comput. Model. 2010, 51, 1375–1382. [CrossRef]10. Suchithra, M.S.; Pai, M.L. Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters. Inf. Process. Agric. 2020, 7, 72–82. [CrossRef]11. Chambers, O. Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method. Sensors 2021, 21, 4208.12. Wu, C.; Chen, Y.; Hong, X.; Liu, Z.; Peng, C. Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques. For. Ecosyst. 2020, 7, 30. [CrossRef]13. Rose, S.; Nickolas, S.; Sangeetha, S. Machine Learning and Statistical Approaches used in Estimating Parameters that Affect the Soil Fertility Status: A Survey. In Proceedings of the 2018 Second International Conference on Green Computing and Internet of Things (ICGCIoT), Karnataka, India, 16–18 August 2018; IEEE: New York, NY, USA, 2018; pp. 381–385.14. Rajamanickam, J. Predictive model construction for prediction of soil fertility using decision tree machine learning algorithm. INFOCOMP J. Comput. Sci. 2021, 20, 49–55.15. Rajamanickam, J.; Mani, S.D. Kullback chi square and Gustafson Kessel probabilistic neural network based soil fertility prediction. Concurr. Comput. Pract. Exp. 2021, 33, e6460. [CrossRef]16. Sirsat, M.S.; Cernadas, E.; Fernández-Delgado, M.; Barro, S. Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods. Comput. Electron. Agric. 2018, 154, 120–133. [CrossRef]17. Ning, J.; Sheng, M.; Yi, X.; Wang, Y.; Hou, Z.; Zhang, Z.; Gu, X. Rapid evaluation of soil fertility in tea plantation based on near-infrared spectroscopy. Spectrosc. Lett. 2018, 51, 463–471. [CrossRef]18. Wang, J.; Wang, Y.; Yang, J. Forecasting of Significant Wave Height Based on Gated Recurrent Unit Network in the Taiwan Straitand Its Adjacent Waters. Water 2021, 13, 86. [CrossRef]19. Hinton, G.E. Deep belief network. Scholarpedia 2009, 4, 5947. [CrossRef]20. Sokkhey, P.; Okazaki, T. Development and Optimization of Deep Belief Networks Applied for Academic Performance Prediction with Larger Datasets. IEIE Trans. Smart Process. Comput. 2020, 9, 298–311. [CrossRef]21. Minh-Tuan, N.; Kim, Y.H. Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems. Appl. Sci. 2019, 9, 3470. [CrossRef]22. Hemeida, M.G.; Ibrahim, A.A.; Mohamed, A.A.A.; Alkhalaf, S.; El-Dine, A.M.B. Optimal allocation of distributed generators DG based Manta Ray Foraging Optimization algorithm (MRFO). Ain Shams Eng. J. 2021, 12, 609–619. [CrossRef]23. Livieris, I.E.; Kanavos, A.; Tampakas, V.; Pintelas, P. A weighted voting ensemble self-labeled algorithm for the detection of lung abnormalities from X-rays. Algorithms 2019, 12, 64. [CrossRef]161712Soil nutrientspH classificationAgricultureSoil managementDeep learningEnsemble modelPublicationORIGINALIntelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques.pdfIntelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques.pdfapplication/pdf2072316https://repositorio.cuc.edu.co/bitstreams/4d72844a-74e5-4988-a16c-98caf3b213fe/download100ce71db12ec14fc3b757b7bfc45fddMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/c6bfa11f-352f-448c-83b9-7e24af785d4f/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTIntelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques.pdf.txtIntelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques.pdf.txttext/plain51621https://repositorio.cuc.edu.co/bitstreams/2cc4899b-dccd-4aa2-ab10-186b6f85e385/downloade668887c7887759f0593f67bba936652MD53THUMBNAILIntelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques.pdf.jpgIntelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques.pdf.jpgimage/jpeg16234https://repositorio.cuc.edu.co/bitstreams/542e3be2-1e64-4280-aad5-c2a66700d0ae/downloadae48fd63a5d6d5d3db10927bc9ea29b2MD5411323/9384oai:repositorio.cuc.edu.co:11323/93842024-09-17 10:17:31.659https://creativecommons.org/licenses/by/4.0/© 2022 by the authors. Licensee MDPI, Basel, Switzerland.open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |