Determination of Organic Carbon Content of the Soils within the Greenhouses Built on Pyroclastic Deposits in Isparta Settlement Area

Sinan Demir; Mehmet Emre Çağ

Makale detayları

Determination of Organic Carbon Content of the Soils within the Greenhouses Built on Pyroclastic Deposits in Isparta Settlement Area

Yazarlar:

Sinan Demir | Mehmet Emre Çağ

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Ziraat Mühendisliği (Diğer)

Dergi:

Black Sea Journal of Agriculture Vol.8 Issue 1, pp.15-28

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001335496

pISSN:

2618-6578

Anahtar kelimeler:

Özet:

 Soil organic carbon SOC is an important indication of soil health and helps to sustain soil fertility. As a result, determining its composition and the factors that influence it is critical for long-term soil nutrient management, especially in controlled conditions such as greenhouses. This study utilizes machine learning to classify SOC content in greenhouses built on pyroclastic deposits in the Isparta region. A dataset of 276 samples and eight variablesclay , silt , sand , soil electrical conductivity EC, pH, elevation, slope, and aspectwere used to model SOC values. SOC content was classified into five classifications: very low 0.6, low 0.6-1.2, medium 1.2-1.8, good 1.8-2.3, and high 2.3. In this study, five machine learning modelsLogistic Regression LR, K-Nearest Neighbors KNN, Support Vector Machine SVM, Decision Tree DT, and Random Forest RFwere evaluated using cross-validation to determine their classification accuracy, precision, recall, F-score, and ROC area. Random Forest RF and Decision Tree DT outperformed the other models, with RF achieving the highest overall accuracy 76.4, precision 77.3, and AUC 0.904, followed by DT at 75.4 and AUC of 0.874. This study shows the practicality of machine learning models in categorizing SOC content, highlighting their importance for long-term soil health and fertility control in greenhouse conditions. To improve model efficacy, future studies should include more auxiliary variables, such as soil physical and chemical qualities and lithological data, as well as a wider range of soil types.

 Soil organic carbon SOC is an important indication of soil health and helps to sustain soil fertility. As a result, determining its composition and the factors that influence it is critical for long-term soil nutrient management, especially in controlled conditions such as greenhouses. This study utilizes machine learning to classify SOC content in greenhouses built on pyroclastic deposits in the Isparta region. A dataset of 276 samples and eight variablesclay , silt , sand , soil electrical conductivity EC, pH, elevation, slope, and aspectwere used to model SOC values. SOC content was classified into five classifications: very low 0.6, low 0.6-1.2, medium 1.2-1.8, good 1.8-2.3, and high 2.3. In this study, five machine learning modelsLogistic Regression LR, K-Nearest Neighbors KNN, Support Vector Machine SVM, Decision Tree DT, and Random Forest RFwere evaluated using cross-validation to determine their classification accuracy, precision, recall, F-score, and ROC area. Random Forest RF and Decision Tree DT outperformed the other models, with RF achieving the highest overall accuracy 76.4, precision 77.3, and AUC 0.904, followed by DT at 75.4 and AUC of 0.874. This study shows the practicality of machine learning models in categorizing SOC content, highlighting their importance for long-term soil health and fertility control in greenhouse conditions. To improve model efficacy, future studies should include more auxiliary variables, such as soil physical and chemical qualities and lithological data, as well as a wider range of soil types.

 Soil organic carbon SOC is an important indication of soil health and helps to sustain soil fertility. As a result, determining its composition and the factors that influence it is critical for long-term soil nutrient management, especially in controlled conditions such as greenhouses. This study utilizes machine learning to classify SOC content in greenhouses built on pyroclastic deposits in the Isparta region. A dataset of 276 samples and eight variablesclay , silt , sand , soil electrical conductivity EC, pH, elevation, slope, and aspectwere used to model SOC values. SOC content was classified into five classifications: very low 0.6, low 0.6-1.2, medium 1.2-1.8, good 1.8-2.3, and high 2.3. In this study, five machine learning modelsLogistic Regression LR, K-Nearest Neighbors KNN, Support Vector Machine SVM, Decision Tree DT, and Random Forest RFwere evaluated using cross-validation to determine their classification accuracy, precision, recall, F-score, and ROC area. Random Forest RF and Decision Tree DT outperformed the other models, with RF achieving the highest overall accuracy 76.4, precision 77.3, and AUC 0.904, followed by DT at 75.4 and AUC of 0.874. This study shows the practicality of machine learning models in categorizing SOC content, highlighting their importance for long-term soil health and fertility control in greenhouse conditions. To improve model efficacy, future studies should include more auxiliary variables, such as soil physical and chemical qualities and lithological data, as well as a wider range of soil types.

 Soil organic carbon SOC is an important indication of soil health and helps to sustain soil fertility. As a result, determining its composition and the factors that influence it is critical for long-term soil nutrient management, especially in controlled conditions such as greenhouses. This study utilizes machine learning to classify SOC content in greenhouses built on pyroclastic deposits in the Isparta region. A dataset of 276 samples and eight variablesclay , silt , sand , soil electrical conductivity EC, pH, elevation, slope, and aspectwere used to model SOC values. SOC content was classified into five classifications: very low 0.6, low 0.6-1.2, medium 1.2-1.8, good 1.8-2.3, and high 2.3. In this study, five machine learning modelsLogistic Regression LR, K-Nearest Neighbors KNN, Support Vector Machine SVM, Decision Tree DT, and Random Forest RFwere evaluated using cross-validation to determine their classification accuracy, precision, recall, F-score, and ROC area. Random Forest RF and Decision Tree DT outperformed the other models, with RF achieving the highest overall accuracy 76.4, precision 77.3, and AUC 0.904, followed by DT at 75.4 and AUC of 0.874. This study shows the practicality of machine learning models in categorizing SOC content, highlighting their importance for long-term soil health and fertility control in greenhouse conditions. To improve model efficacy, future studies should include more auxiliary variables, such as soil physical and chemical qualities and lithological data, as well as a wider range of soil types.

Makale detayları

Determination of Organic Carbon Content of the Soils within the Greenhouses Built on Pyroclastic Deposits in Isparta Settlement Area

Yazarlar:

Sinan Demir | Mehmet Emre Çağ

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Ziraat Mühendisliği (Diğer)

Dergi:

Black Sea Journal of Agriculture Vol.8 Issue 1, pp.15-28

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001335496

pISSN:

2618-6578

Soil organic carbon

Machine learning

Greenhouses

Topography

Soil properties

Volcanic materials

Sayı

Makale detayları

Determination of Organic Carbon Content of the Soils within the Greenhouses Built on Pyroclastic Deposits in Isparta Settlement Area

Yazarlar: Sinan Demir | Mehmet Emre Çağ

Yayıncı: Karyay Karadeniz Yayımcılık

Ana Başlıklar: Ziraat Mühendisliği (Diğer)

Dergi: Black Sea Journal of Agriculture Vol.8 Issue 1, pp.15-28

Belge Tipi: Makale

Yayın Tarihi: 1/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001335496

pISSN: 2618-6578

Soil organic carbon

Machine learning

Greenhouses

Topography

Soil properties

Volcanic materials

Sayı

Yazarlar:

Sinan Demir | Mehmet Emre Çağ

Yayıncı:

Karyay Karadeniz Yayımcılık

Ana Başlıklar:

Ziraat Mühendisliği (Diğer)

Dergi:

Black Sea Journal of Agriculture Vol.8 Issue 1, pp.15-28

Belge Tipi:

Makale

Yayın Tarihi:

1/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001335496

pISSN:

2618-6578