Land Quality Index for Paddy (Oryza sativa L.) Cultivation Area Based on Deep Learning Approach using Geographical Information System and Geostatistical Techniques

Nurettin Şenyer; Hasan Akay; Mehmet Serhat Odabas; Orhan Dengiz

Makale detayları

Land Quality Index for Paddy (Oryza sativa L.) Cultivation Area Based on Deep Learning Approach using Geographical Information System and Geostatistical Techniques

Yazarlar:

Nurettin Şenyer | Hasan Akay | Mehmet Serhat Odabas | Orhan Dengiz

Yayıncı:

Van Yuzuncu Yıl University

Ana Başlıklar:

Agricultural | Veterinary and Food Sciences

Dergi:

Yuzuncu Yıl University Journal of Agricultural Sciences Vol.33 Issue 1, pp.75-90

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001286608

pISSN:

1308-7576

Anahtar kelimeler:

Özet:

 Türkiye has ideal ecological conditions for growing rice, and its yield per hectare is often higher than the average worldwide. However, unbalanced fertilization, nutrient deficiency, and irrigation problems negatively affect paddy production when soil characteristics are not considered. The present study was conducted on a 1763-hectare field 652000-659000E-W and 4528000-4536000N-S in 2019. This studys primary goal was to categorize land quality for rice production using 15 different physicochemical parameters and a GIS Geographical Information Systems and deep learning DL technique. Using these parameters soil types were classified and regression analysis was performed by DL. Different soil parameters as network outputs used in this study caused different performance levels in models. Therefore, different models were suggested for each network output. The R2 values indicated a respectable level for parameter prediction, and an accuracy of 88 was attained when classifying class data. The findings of the study demonstrated that deep learning may be used to forecast soil metrics and distinguish between different land quality classes. Additionally, a field investigation was used to validate the indicated land quality classifications. Using statistical techniques, a substantial positive link between rice yield and land quality classes was discovered.

 Türkiye has ideal ecological conditions for growing rice, and its yield per hectare is often higher than the average worldwide. However, unbalanced fertilization, nutrient deficiency, and irrigation problems negatively affect paddy production when soil characteristics are not considered. The present study was conducted on a 1763-hectare field 652000-659000E-W and 4528000-4536000N-S in 2019. This studys primary goal was to categorize land quality for rice production using 15 different physicochemical parameters and a GIS Geographical Information Systems and deep learning DL technique. Using these parameters soil types were classified and regression analysis was performed by DL. Different soil parameters as network outputs used in this study caused different performance levels in models. Therefore, different models were suggested for each network output. The R2 values indicated a respectable level for parameter prediction, and an accuracy of 88 was attained when classifying class data. The findings of the study demonstrated that deep learning may be used to forecast soil metrics and distinguish between different land quality classes. Additionally, a field investigation was used to validate the indicated land quality classifications. Using statistical techniques, a substantial positive link between rice yield and land quality classes was discovered.

 Türkiye has ideal ecological conditions for growing rice, and its yield per hectare is often higher than the average worldwide. However, unbalanced fertilization, nutrient deficiency, and irrigation problems negatively affect paddy production when soil characteristics are not considered. The present study was conducted on a 1763-hectare field 652000-659000E-W and 4528000-4536000N-S in 2019. This studys primary goal was to categorize land quality for rice production using 15 different physicochemical parameters and a GIS Geographical Information Systems and deep learning DL technique. Using these parameters soil types were classified and regression analysis was performed by DL. Different soil parameters as network outputs used in this study caused different performance levels in models. Therefore, different models were suggested for each network output. The R2 values indicated a respectable level for parameter prediction, and an accuracy of 88 was attained when classifying class data. The findings of the study demonstrated that deep learning may be used to forecast soil metrics and distinguish between different land quality classes. Additionally, a field investigation was used to validate the indicated land quality classifications. Using statistical techniques, a substantial positive link between rice yield and land quality classes was discovered.

 Türkiye has ideal ecological conditions for growing rice, and its yield per hectare is often higher than the average worldwide. However, unbalanced fertilization, nutrient deficiency, and irrigation problems negatively affect paddy production when soil characteristics are not considered. The present study was conducted on a 1763-hectare field 652000-659000E-W and 4528000-4536000N-S in 2019. This studys primary goal was to categorize land quality for rice production using 15 different physicochemical parameters and a GIS Geographical Information Systems and deep learning DL technique. Using these parameters soil types were classified and regression analysis was performed by DL. Different soil parameters as network outputs used in this study caused different performance levels in models. Therefore, different models were suggested for each network output. The R2 values indicated a respectable level for parameter prediction, and an accuracy of 88 was attained when classifying class data. The findings of the study demonstrated that deep learning may be used to forecast soil metrics and distinguish between different land quality classes. Additionally, a field investigation was used to validate the indicated land quality classifications. Using statistical techniques, a substantial positive link between rice yield and land quality classes was discovered.

Makale detayları

Land Quality Index for Paddy (Oryza sativa L.) Cultivation Area Based on Deep Learning Approach using Geographical Information System and Geostatistical Techniques

Yazarlar:

Nurettin Şenyer | Hasan Akay | Mehmet Serhat Odabas | Orhan Dengiz

Yayıncı:

Van Yuzuncu Yıl University

Ana Başlıklar:

Agricultural | Veterinary and Food Sciences

Dergi:

Yuzuncu Yıl University Journal of Agricultural Sciences Vol.33 Issue 1, pp.75-90

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001286608

pISSN:

1308-7576

Deep learning

Mapping

Orzya sativa L.

Paddy

Precision agriculture

Sayı

Makale detayları

Land Quality Index for Paddy (Oryza sativa L.) Cultivation Area Based on Deep Learning Approach using Geographical Information System and Geostatistical Techniques

Yazarlar: Nurettin Şenyer | Hasan Akay | Mehmet Serhat Odabas | Orhan Dengiz

Yayıncı: Van Yuzuncu Yıl University

Ana Başlıklar: Agricultural | Veterinary and Food Sciences

Dergi: Yuzuncu Yıl University Journal of Agricultural Sciences Vol.33 Issue 1, pp.75-90

Belge Tipi: Makale

Yayın Tarihi: 3/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001286608

pISSN: 1308-7576

Deep learning

Mapping

Orzya sativa L.

Paddy

Precision agriculture

Sayı

Yazarlar:

Nurettin Şenyer | Hasan Akay | Mehmet Serhat Odabas | Orhan Dengiz

Yayıncı:

Van Yuzuncu Yıl University

Ana Başlıklar:

Agricultural | Veterinary and Food Sciences

Dergi:

Yuzuncu Yıl University Journal of Agricultural Sciences Vol.33 Issue 1, pp.75-90

Belge Tipi:

Makale

Yayın Tarihi:

3/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001286608

pISSN:

1308-7576