An Accurate Approach for Predicting Soil Quality Based on Machine Learning in Drylands

Radwa A. El Behairy, Hasnaa M. El Arwash, Ahmed A. El Baroudy, Mahmoud M. Ibrahim, Elsayed Said Mohamed, Nazih Y. Rebouh and Mohamed S. Shokr ()
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Radwa A. El Behairy: Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt
Hasnaa M. El Arwash: Mechatronics Engineering Department, Alexandria Higher Institute of Engineering & Technology (AIET), Alexandria 21311, Egypt
Ahmed A. El Baroudy: Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt
Mahmoud M. Ibrahim: Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt
Elsayed Said Mohamed: National Authority for Remote Sensing and Space Sciences, Cairo 1564, Egypt
Nazih Y. Rebouh: Department of Environmental Management, Institute of Environmental Engineering (RUDN University), Moscow 117198, Russia
Mohamed S. Shokr: Soil and Water Department, Faculty of Agriculture, Tanta University, Tanta 31527, Egypt

Agriculture, 2024, vol. 14, issue 4, 1-24

Abstract: Nowadays, machine learning (ML) is a useful technology due to its high accuracy in constructing non-linear models and algorithms that can adapt to the complexity and diversity of data. Thus, the current work aimed to predict the soil quality index (SQI) from extensive soil data, achieving high accuracy with the artificial neural networks (ANN) model. However, the efficiency of ANN depends on the accuracy of the data that is prepared for training. For this purpose, MATLAB programming language was used to enable the calculation, classification, and compilation of the results into databases within a few minutes. The proposed MATLAB program was highly efficient, accurate, and quick in calculating soil big data for training the machine compared with traditional methods. The database contains 306 vector sets, 80% of them are used for training and the remaining 20% are reserved for testing. The optimal model obtained comprises one hidden layer with 250 neurons and one output layer with a sigmoid function. The ANN achieved a high coefficient of determination (R 2 ) values for SQI estimation, with around 0.97 and 0.98 for training and testing, respectively. The results indicate that 36.93% of the total soil samples belonged to the very high quality class (C1). In contrast, the high quality (C2), moderate quality (C3), low quality (C4), and very low quality (C5) classes accounted for 10.46%, 31.37%, 20.92%, and 0.33% of the samples, respectively. The high contents of CaCO 3 , pH, sodium saturation, salinity, and clay content were identified as limiting factors in certain areas. The results of this study indicated high accuracy of soil quality assessment using physical, chemical, and fertility soil features in regression analysis with ANN. This method, which is suitable for arid zones, enhances agricultural productivity and decision-making by identifying critical soil quality categories and constraints.

Keywords: arid zones; artificial neural networks; big data; MATLAB; soil quality index (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: 2024
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