Soil quality assessment based on machine learning approach for cultivated lands in semi-humid environmental condition part of Black Sea region

Alaboz, Pelin; Odabas, Mehmet Serhat; Dengiz, Orhan

doi:10.1080/03650340.2023.2248002

Cited by 11 publications

(6 citation statements)

References 64 publications

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“…Soil quality (SQ) is a key indicator in assessing the sustainable use of soil, and the accuracy of the evaluation of SQ is important for us to make well-informed decisions to maintain high soil productivity and prevent soil degradation [6]. The SQ is an integrated expression of soil properties, and using more soil properties to assess SQ can produce more accurate results [7][8][9][10]. However, using more soil properties for SQ evaluation could significantly increase the cost of labor, material, and financial resources in laboratory analysis [6,8].…”

Section: Introductionmentioning

confidence: 99%

“…The SQ is an integrated expression of soil properties, and using more soil properties to assess SQ can produce more accurate results [7][8][9][10]. However, using more soil properties for SQ evaluation could significantly increase the cost of labor, material, and financial resources in laboratory analysis [6,8]. Therefore, many conceptual models and approaches have been established to assess SQ under different regions in the world [2,11,12].…”

Section: Introductionmentioning

confidence: 99%

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Influence of Land Use Types on Soil Properties and Soil Quality in Karst Regions of Southwest China

Ma,

Si,

et al. 2024

Agronomy

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Establishing a suitable and useful soil quality index (SQI) is the key to accurately evaluating changes in soil quality (SQ) under different land use types. In the present study, a suitable and useful SQI using a minimum data set (MDS) with two scoring methods (linear scoring method and nonlinear scoring method) and two additive models (simple additive model with same weighting value and weighted additive model with significant different weighting value) was established to compare SQ under different land uses in Longtan valley. Soil samples were collected under one dryland (DRYL), one paddy (PADD), one orchard (GRA), and one natural forest (FORE), and 13 soil properties were measured. The four land use types had the same soil type and similar environmental conditions. Land use types had significant effects on the measured 12 soil properties. The top two principal components in Principal Component Analysis were chosen, and their cumulative variance was more than 90%. Soil indicators of soil labile carbon, C/N ratio, and microaggregates were chosen as members of MDS in this study. Significant (p < 0.001) positive correlations among the four establishing SQIs were found. The values of the sensitive index ranged from 47.17% to 82.12% for the four SQIs, and the SQI established using the nonlinear scoring method and weighted additive model (SQI-NLW) had the highest values. Among the four land use types, the four SQIs had similar change trends, and the average values of SQ under FORE (0.73) and PADD (0.68) were significantly higher than those under GRA (0.54) and DRYL (0.43). These results indicated that the SQI-NLW was an effective and precise tool to assess SQ under different land uses in similar regions, and the FORE and PADD were the suitable land use types for the sustainable use of soils in karst regions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of Land Use Types on Soil Properties and Soil Quality in Karst Regions of Southwest China

Ma,

Si,

et al. 2024

Agronomy

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“…ML algorithms utilize past experiments to establish successful relationships for data inputs, rebuild the knowledge schema, and process them for future prediction [15,16]. They are used in agriculture [17][18][19], especially in determining soil quality [20][21][22], to enhance efficiency, reduce production costs, and minimize environmental impact [23,24]. Machine learning methods can be supervised [25], unsupervised [26], or reinforcement learning [27].…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning methods can be supervised [25], unsupervised [26], or reinforcement learning [27]. Artificial neural networks (ANN) are an example of an ML method under supervised learning and can be used to optimize agricultural management [16,22,28]. Supervised learning builds a model to predict the output from input data.…”

Section: Introductionmentioning

confidence: 99%

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

El Behairy,

El Arwash,

El Baroudy

et al. 2024

Agriculture

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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 (R2) 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 CaCO3, 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.

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Soil System Status, Threats, Utilization Pattern, and Sustainable Management Strategies in the Global South: African Perspectives

Mureithi,

Mwendwa,

Kamau

2024

Sustainable Soil Systems in Global South

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Soil quality assessment based on machine learning approach for cultivated lands in semi-humid environmental condition part of Black Sea region

Cited by 11 publications

References 64 publications

Influence of Land Use Types on Soil Properties and Soil Quality in Karst Regions of Southwest China

Influence of Land Use Types on Soil Properties and Soil Quality in Karst Regions of Southwest China

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

Soil System Status, Threats, Utilization Pattern, and Sustainable Management Strategies in the Global South: African Perspectives

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