Intelligent Agricultural Modelling of Soil Nutrients and pH Classification Using Ensemble Deep Learning Techniques

José Escorcia-Gutierrez, Margarita Gamarra, Roosvel Soto-Diaz, Meglys Pérez, Natasha Madera and Romany F. Mansour
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José Escorcia-Gutierrez: Electronics and Telecommunications Engineering Program, Universidad Autónoma del Caribe, Barranquilla 080020, Colombia
Margarita Gamarra: Departament of Computational Science and Electronic, Universidad de la Costa, CUC, Barranquilla 080002, Colombia
Roosvel Soto-Diaz: Biomedical Engineering Program, Universidad Simón Bolívar, Barranquilla 080001, Colombia
Meglys Pérez: Electronics and Telecommunications Engineering Program, Universidad Autónoma del Caribe, Barranquilla 080020, Colombia
Natasha Madera: Electronics and Telecommunications Engineering Program, Universidad Autónoma del Caribe, Barranquilla 080020, Colombia
Romany F. Mansour: Department of Mathematics, Faculty of Science, New Valley University, El-Kharga 72511, Egypt

Agriculture, 2022, vol. 12, issue 7, 1-16

Abstract: 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.

Keywords: soil nutrients; pH classification; agriculture; soil management; deep learning; ensemble model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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