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

Year 2023, Volume: 33 Issue: 1, 75 - 90, 31.03.2023

Nurettin Şenyer Hasan Akay Mehmet Serhat Odabas Orhan Dengiz Saravanan Sıvarajan

https://doi.org/10.29133/yyutbd.1177796

Abstract

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 study's 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.

Keywords

Deep learning Mapping Orzya sativa L. Paddy Precision agriculture

Supporting Institution

TUBITAK

Project Number

107O443

References

  • Aggarwal, N., & Agrawal, R. K. (2012). First and second order statistics features for classification of magnetic resonance brain images. Journal of Signal and Information Processing, 3, (pp. 146-153).
  • Akay, H., Sezer, I., Mut, Z., & Dengiz, O. (2017). Yield and Quality Performance of Some Paddy Cultivars Grown in Left Bank of Bafra Plain. KSU Journal of Natural Science, 20 (special issue), 297-302.
  • Araus, J. L., & Cairns, J. E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends in plant science, 19(1), 52–61.
  • Azizi, A., Gilandeh, Y. A., Mesri-Gundoshmian, T., Saleh-Bigdeli, A. A., & Moghaddam, H. A. (2020). Classification of soil aggregates: A novel approach based on deep learning. Soil and Tillage Research, 199,104586.
  • Bunting, E. S. (1981). Assessments of the effects on yield of variations in climate and soil characteristics for twenty crop species. Bogor (Indonesia): Centre for Soil Research. UNDP/FAO, AGOF/INS/78/006 Technical Note, No: 12.
  • Chollet, F. (2020). The Sequential model. https://keras.io/guides/sequential_model/. (Online accessed Sept 4th 2020).
  • Dengiz, O. (2010). Morphology, Physico-Chemical Properties and Classification of Soils on Terraces of the Tigris River in the South-East Anatolia Region of Turkey. Journal of Agricultural Sciences, 16 (3), 205-212.
  • Dengiz, O. (2013). Land Suitability Assessment for Rice Cultivation Based on GIS Modeling. Turkish Journal of Agriculture and Forestry, 37, 326-334.
  • Dengiz, O. (2020). Soil quality index for paddy fields based on standard scoring functions and weight allocation method. Archives of Agronomy and Soil Science, 66(3), 301-315.
  • Dengiz, O., Gol, C., Ekberli, I., & Ozdemir, N. (2009). Determination of distribution and properties of soil formed on diffirent alluviyal terraces. Anadolu Journal of Agricultural Sciences, 24(3), 184-193.
  • Dengiz, O., Ozyazıcı, M. A,, & Saglam, M. (2015). Multi-criteria assessment and geostatistical approach for determination of rice growing suitability sites in Gokirmak Catchment. Paddy Water Envirn. 13, 1–10.
  • DMI, (2019). Turkish state meteorological service. Ankara,Turkey. (Accessed October 4, 2020).
  • Esgario, J. G., Krohling, R. A., & Ventura, J. A. (2020). Deep learning for classification and severity estimation of coffee leaf biotic stress. Computers and Electronics in Agriculture, 169, 105162.
  • FAO, (1983). Guidelines. Land Evaluation for Rainfed Agriculture. Rome: FAO Soils Bulletin No. 52. (Accessed October 4, 2020).
  • FAO, (1985). Guidelines. Land Evaluation for Irrigated Agriculture. Rome: FAO Soils Bulletin No. 55. (Accessed October 4, 2020).
  • Garris, A. J., Tai, T. H., Coburn, J., Kresovich, S., & McCouch, S. (2005). Genetic Structure and Diversity in Oryza sativa L. Genetics, 169(3), 1631-1638.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge: MIT Press, USA.
  • Google-Colab, (2020). Google Colaberatory platform. https://colab.research.google.com. (Accessed September 4, 2020).
  • Goovaerts, P. (1999). Using elevation to aid the geostatistical mapping of rain fall erosivity. Catena 34, 227–242.
  • Gupta, R. P., & Abrol, I. P. (1993). A study of some tillage practices for sustainable crop production in India. Soil Till. Res, 27,253–273.
  • IBM, (2015). Released 2015. IBM SPSS Statistics for Windows, Version 23.0. IBM Corp., Armonk, NY.
  • Jagadish, S. V. K., Craufurd, P. Q., & Wheeler, T. R. (2007). High temperature stress and spikelet fertility in rice (Oryza sativa L.). Journal of Experimental Botany, 58(7), 1627–1635.
  • Kamilaris, A., & Prenafeta-Boldu, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147,70-90.
  • Keras, (2020). https:/keras.io/. (Accessed September 4, 2020).
  • Li, X., Li, H., Yang, L., & Ren, Y. (2018). Assessment of soil quality of croplands in the corn belt of Northeast China. Sustainability, 1,1–16.
  • Meral, R., & Temizel, K. E. (2006). Irrigation Applications and Efficient Water Use in Rice Production. KSU. Journal of Science and Engineering 9(2),104-109.
  • Mongkolsawat, C., Thirangoon, P., & Kuptawutinan, P. (2002). A physical evaluation of land suitability for rice: a methodological study using GIS. Khon Kaen (Thailand): Computer Centre, Khon Kaen University, Thailand.
  • Moron, A. (2005). Indicadores para el diagno´ stico de la calidad de suelos en sistemas agrı´colas. In: Marelli, H.J. (Ed.), Indicadores de Calidad de Suelo. Seminario Internacional. Marcos Jua´rez, Argentina.
  • Mulla, D. J., & McBrathey, A. B. (2000). Soil Spatial Variability A-321-A-351, In: Handbook of Soil Science, Malcolm E. Sumner (Ed. In Chief) CRS Press, USA.
  • Mwendwa, S. M., Mbuvi, J. P., & Kironchi, G. (2019). Land evaluation for crop production in Upper Kabete Campus feld, University of Nairobi, Kenya. Chem. Biol. Technol. Agric., 6(16), 2-10.
  • Nath, J. A., Charyya, T. B., Ray, S. K., Deka, J., Das, A., & Devi, H. (2016). Assessment of rice farming management practices based on soil organic carbon pool analysis. Trop Ecol. 57, 607–611.
  • Ozkan, B., Dengiz, O., & Demirag Turan, I. (2019). Site suitability assessment and mapping for rice cultivation using multi-criteria decision analysis based on fuzzy-AHP and TOPSIS approaches under semihumid ecological condition in delta plain. Paddy Water Environ. 17,655-676.
  • Padarian, J., Minasny, B., & McBratney, A. B. (2019). Using deep learning to predict soil properties from regional spectral data. Geoderma Regional, 16, e00198.
  • Rezaee, L., Moosavi, A. A., Davatgar, N., & Sepaskhan, A. R. (2020). Soil quality indices of paddy soils in Guilan province of northern Iran: Spatial variability and their influential parameters. Ecological Indicators, 117, 106566.
  • Sezer, I., & Dengiz, O. (2014). Application of multi-criteria decision making approach for rice land suitability analysis in Turkey. Turkish Journal of Agriculture and Forestry, 1, 926-934.
  • Sezer, I., Senocak, H. S., & Akay, A. (2017). Comparison of Transplanting and Broadcasting Methods ın Some Paddy Cultivars. KSU Journal of Natural Science, 20 (Special Issue),292-296.
  • Shrivastava, V. K., Pradhan, M. K., Minz, S., & Thakur, M. P. 2019. Rice Plant Disease Classification Using Transfer Learning Of Deep Convolution Neural Network. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 1, 631-635.
  • Sirat, A., Sezer, I., & Akay, H. (2012). Organic rice farming in the Kızılırmak Delta. Gumushane University Journal of Science and Technology Institute, 2(2),76-92.
  • Sys, C., Van Ranst, E., Debaveye, I. J., & Beernaert, F. (1993). Land evaluation. Part III: Crop Requirements. General Administration for Development Cooperation, Agricultural publication-No. 7, pp.199, Brussels-Belgium.
  • Wilding, L. P., Bouma, J., & Goss, D. W. (1994). Impact of spatial variability on interpretive modeling. In: Bryant RB, Arnold RW, editors. Quantitative modeling of soil forming processes (Vol. 39). Madison: SSSA Special Publication, 65–75.

Year 2023, Volume: 33 Issue: 1, 75 - 90, 31.03.2023

Nurettin Şenyer Hasan Akay Mehmet Serhat Odabas Orhan Dengiz Saravanan Sıvarajan

https://doi.org/10.29133/yyutbd.1177796

Abstract

Project Number

107O443

References

  • Aggarwal, N., & Agrawal, R. K. (2012). First and second order statistics features for classification of magnetic resonance brain images. Journal of Signal and Information Processing, 3, (pp. 146-153).
  • Akay, H., Sezer, I., Mut, Z., & Dengiz, O. (2017). Yield and Quality Performance of Some Paddy Cultivars Grown in Left Bank of Bafra Plain. KSU Journal of Natural Science, 20 (special issue), 297-302.
  • Araus, J. L., & Cairns, J. E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends in plant science, 19(1), 52–61.
  • Azizi, A., Gilandeh, Y. A., Mesri-Gundoshmian, T., Saleh-Bigdeli, A. A., & Moghaddam, H. A. (2020). Classification of soil aggregates: A novel approach based on deep learning. Soil and Tillage Research, 199,104586.
  • Bunting, E. S. (1981). Assessments of the effects on yield of variations in climate and soil characteristics for twenty crop species. Bogor (Indonesia): Centre for Soil Research. UNDP/FAO, AGOF/INS/78/006 Technical Note, No: 12.
  • Chollet, F. (2020). The Sequential model. https://keras.io/guides/sequential_model/. (Online accessed Sept 4th 2020).
  • Dengiz, O. (2010). Morphology, Physico-Chemical Properties and Classification of Soils on Terraces of the Tigris River in the South-East Anatolia Region of Turkey. Journal of Agricultural Sciences, 16 (3), 205-212.
  • Dengiz, O. (2013). Land Suitability Assessment for Rice Cultivation Based on GIS Modeling. Turkish Journal of Agriculture and Forestry, 37, 326-334.
  • Dengiz, O. (2020). Soil quality index for paddy fields based on standard scoring functions and weight allocation method. Archives of Agronomy and Soil Science, 66(3), 301-315.
  • Dengiz, O., Gol, C., Ekberli, I., & Ozdemir, N. (2009). Determination of distribution and properties of soil formed on diffirent alluviyal terraces. Anadolu Journal of Agricultural Sciences, 24(3), 184-193.
  • Dengiz, O., Ozyazıcı, M. A,, & Saglam, M. (2015). Multi-criteria assessment and geostatistical approach for determination of rice growing suitability sites in Gokirmak Catchment. Paddy Water Envirn. 13, 1–10.
  • DMI, (2019). Turkish state meteorological service. Ankara,Turkey. (Accessed October 4, 2020).
  • Esgario, J. G., Krohling, R. A., & Ventura, J. A. (2020). Deep learning for classification and severity estimation of coffee leaf biotic stress. Computers and Electronics in Agriculture, 169, 105162.
  • FAO, (1983). Guidelines. Land Evaluation for Rainfed Agriculture. Rome: FAO Soils Bulletin No. 52. (Accessed October 4, 2020).
  • FAO, (1985). Guidelines. Land Evaluation for Irrigated Agriculture. Rome: FAO Soils Bulletin No. 55. (Accessed October 4, 2020).
  • Garris, A. J., Tai, T. H., Coburn, J., Kresovich, S., & McCouch, S. (2005). Genetic Structure and Diversity in Oryza sativa L. Genetics, 169(3), 1631-1638.
  • Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge: MIT Press, USA.
  • Google-Colab, (2020). Google Colaberatory platform. https://colab.research.google.com. (Accessed September 4, 2020).
  • Goovaerts, P. (1999). Using elevation to aid the geostatistical mapping of rain fall erosivity. Catena 34, 227–242.
  • Gupta, R. P., & Abrol, I. P. (1993). A study of some tillage practices for sustainable crop production in India. Soil Till. Res, 27,253–273.
  • IBM, (2015). Released 2015. IBM SPSS Statistics for Windows, Version 23.0. IBM Corp., Armonk, NY.
  • Jagadish, S. V. K., Craufurd, P. Q., & Wheeler, T. R. (2007). High temperature stress and spikelet fertility in rice (Oryza sativa L.). Journal of Experimental Botany, 58(7), 1627–1635.
  • Kamilaris, A., & Prenafeta-Boldu, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147,70-90.
  • Keras, (2020). https:/keras.io/. (Accessed September 4, 2020).
  • Li, X., Li, H., Yang, L., & Ren, Y. (2018). Assessment of soil quality of croplands in the corn belt of Northeast China. Sustainability, 1,1–16.
  • Meral, R., & Temizel, K. E. (2006). Irrigation Applications and Efficient Water Use in Rice Production. KSU. Journal of Science and Engineering 9(2),104-109.
  • Mongkolsawat, C., Thirangoon, P., & Kuptawutinan, P. (2002). A physical evaluation of land suitability for rice: a methodological study using GIS. Khon Kaen (Thailand): Computer Centre, Khon Kaen University, Thailand.
  • Moron, A. (2005). Indicadores para el diagno´ stico de la calidad de suelos en sistemas agrı´colas. In: Marelli, H.J. (Ed.), Indicadores de Calidad de Suelo. Seminario Internacional. Marcos Jua´rez, Argentina.
  • Mulla, D. J., & McBrathey, A. B. (2000). Soil Spatial Variability A-321-A-351, In: Handbook of Soil Science, Malcolm E. Sumner (Ed. In Chief) CRS Press, USA.
  • Mwendwa, S. M., Mbuvi, J. P., & Kironchi, G. (2019). Land evaluation for crop production in Upper Kabete Campus feld, University of Nairobi, Kenya. Chem. Biol. Technol. Agric., 6(16), 2-10.
  • Nath, J. A., Charyya, T. B., Ray, S. K., Deka, J., Das, A., & Devi, H. (2016). Assessment of rice farming management practices based on soil organic carbon pool analysis. Trop Ecol. 57, 607–611.
  • Ozkan, B., Dengiz, O., & Demirag Turan, I. (2019). Site suitability assessment and mapping for rice cultivation using multi-criteria decision analysis based on fuzzy-AHP and TOPSIS approaches under semihumid ecological condition in delta plain. Paddy Water Environ. 17,655-676.
  • Padarian, J., Minasny, B., & McBratney, A. B. (2019). Using deep learning to predict soil properties from regional spectral data. Geoderma Regional, 16, e00198.
  • Rezaee, L., Moosavi, A. A., Davatgar, N., & Sepaskhan, A. R. (2020). Soil quality indices of paddy soils in Guilan province of northern Iran: Spatial variability and their influential parameters. Ecological Indicators, 117, 106566.
  • Sezer, I., & Dengiz, O. (2014). Application of multi-criteria decision making approach for rice land suitability analysis in Turkey. Turkish Journal of Agriculture and Forestry, 1, 926-934.
  • Sezer, I., Senocak, H. S., & Akay, A. (2017). Comparison of Transplanting and Broadcasting Methods ın Some Paddy Cultivars. KSU Journal of Natural Science, 20 (Special Issue),292-296.
  • Shrivastava, V. K., Pradhan, M. K., Minz, S., & Thakur, M. P. 2019. Rice Plant Disease Classification Using Transfer Learning Of Deep Convolution Neural Network. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 1, 631-635.
  • Sirat, A., Sezer, I., & Akay, H. (2012). Organic rice farming in the Kızılırmak Delta. Gumushane University Journal of Science and Technology Institute, 2(2),76-92.
  • Sys, C., Van Ranst, E., Debaveye, I. J., & Beernaert, F. (1993). Land evaluation. Part III: Crop Requirements. General Administration for Development Cooperation, Agricultural publication-No. 7, pp.199, Brussels-Belgium.
  • Wilding, L. P., Bouma, J., & Goss, D. W. (1994). Impact of spatial variability on interpretive modeling. In: Bryant RB, Arnold RW, editors. Quantitative modeling of soil forming processes (Vol. 39). Madison: SSSA Special Publication, 65–75.
There are 40 citations in total.

Details

Primary Language English
Subjects Agricultural, Veterinary and Food Sciences
Journal Section Articles
Authors

Nurettin Şenyer 0000-0001-8668-5263

Hasan Akay 0000-0003-1198-8686

Mehmet Serhat Odabas 0000-0002-1863-7566

Orhan Dengiz 0000-0002-0458-6016

Saravanan Sıvarajan This is me 0000-0002-6846-4683

Project Number 107O443
Publication Date March 31, 2023
Acceptance Date December 5, 2022
Published in Issue Year 2023 Volume: 33 Issue: 1

Cite

APA Şenyer, N., Akay, H., Odabas, M. S., Dengiz, O., et al. (2023). Land Quality Index for Paddy (Oryza sativa L.) Cultivation Area Based on Deep Learning Approach using Geographical Information System and Geostatistical Techniques. Yuzuncu Yıl University Journal of Agricultural Sciences, 33(1), 75-90. https://doi.org/10.29133/yyutbd.1177796
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Yuzuncu Yil University Journal of Agricultural Sciences by Van Yuzuncu Yil University Faculty of Agriculture is licensed under a Creative Commons Attribution 4.0 International License.