Machine learning algorithms and geographic information system techniques to predict land suitability maps for wheat cultivation in the Central Anatolia Region

Aljanabi, Firas K.; Dedeoğlu, Mert

doi:10.12911/22998993/196044

Powiadomienia systemowe

Sesja wygasła!

Artykuł - szczegóły

Tytuł artykułu

Machine learning algorithms and geographic information system techniques to predict land suitability maps for wheat cultivation in the Central Anatolia Region

Autorzy

Aljanabi Firas K. , Dedeoğlu Mert

Treść / Zawartość

Pełne teksty:

Pobierz

Identyfikatory

DOI

10.12911/22998993/196044

Warianty tytułu

Języki publikacji

Abstrakty

Maintaining food security through increased agricultural production is a major concern for decision-makers, especially in areas with arid and semi-arid climatic conditions and limited natural resources. Land suitability prediction for cultivating strategic crops, including wheat, has emerged as a crucial subject for academics, decision-makers, and economists to ensure the sustainability of natural resources. This paper aims to use three soil morphological parameters, three soil physical parameters, four soil chemical parameters, and a long-term remote sensing index as input factors to produce land suitability maps for wheat cultivation based on five machine learning algorithms (MLAs): ANN, KNN, RF, SVM, and XgbTree, in the Gozlu agricultural enterprise, which is located in a semi-arid region of the Central Anatolian Plateau. To achieve this target, an inventory of 238 appropriateness points for cultivated wheat has been executed over five years, from 2019 to 2023. The outcomes revealed that the soil texture and soli available water capacity parameters were the most influential in land suitability prediction. The best performance among the MLAs was achieved by the XgbTree algorithm, which had an accuracy of 0.98 and a kappa coefficient of 0.81. Additionally, the area under the curve (AUC) was 0.90 according the receiver operating characteristics (ROC) curve approach. The results of the study demonstrated an excellent ability of the MLAs to predict land suitability for wheat cultivation in semi-arid climate conditions. This approach can play a significant role in ensuring food security and serves as an important tool for decision-makers in sustainable development. However, we propose that the approach should be examined in comparable climatic conditions with diverse crops to ensure it is a viable solution with widely cases.

Słowa kluczowe

machine learning random forest support vector machine land suitability remote sensing GIS wheat

Wydawca

Polskie Towarzystwo Inżynierii Ekologicznej
Lublin University of Technology

Czasopismo

Journal of Ecological Engineering

Rocznik

2025

Tom

Vol. 26, nr 1

Strony

373--387

Opis fizyczny

Bibliogr. 57 poz., rys., tab.

Twórcy

autor

Aljanabi Firas K.

198146001008@ogr.selcuk.edu.tr

Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Selcuk University, Konya, Turkey

https://orcid.org/0000-0002-5122-3699

autor

Dedeoğlu Mert

Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Selcuk University, Konya, Turkey

https://orcid.org/0000-0002-5122-3699

Bibliografia

1. Abakay, O., Kılıç, M., Günal, H., & Kılıç, O. M. (2024). Tree-based algorithms for spatial modeling of soil particle distribution in arid and semi-arid region. Environmental monitoring and assessment, 196(3), 264. https://doi.org/ https://doi.org/10.1007/s10661-024-12431-6
2. Abdikan, S., Sekertekin, A., Madenoglu, S., Ozcan, H., Peker, M., Pinar, M. O., Koc, A., Akgul, S., Secmen, H., Kececi, M., Tuncay, T., & Balik Sanli, F. (2023). Surface soil moisture estimation from multi-frequency SAR images using ANN and experimental data on a semi-arid environment region in Konya, Turkey. Soil and Tillage Research, 228, 105646. https://doi.org/https://doi.org/10.1016/j.still.2023.105646
3. Akumu, C., Johnson, J., Etheridge, D., Uhlig, P., Woods, M., Pitt, D., & McMurray, S. (2015). GIS-fuzzy logic based approach in modeling soil texture: Using parts of the Clay Belt and Hornepayne region in Ontario Canada as a case study. Geoderma, 239, 13–24. https://doi.org/https://doi.org/10.1016/j. geoderma.2014.09.021
4. Altay, F., Bağcı, S. A., Yazar, S., Belen, S., Öztürk, İ., Küçüközdemir, Ü., & Zencirci, N. (2024). Winter wheat research in Türkiye. In N. Zencirci, F. Altay, F. S. Baloch, M. A. Nadeem, & N. Ludidi (Eds.), Advances in Wheat Breeding: Towards Climate Resilience and Nutrient Security (23–101). Springer Nature Singapore. https://doi.org/https://doi.org/10.1007/978-981-99-9478-6_2
5. Ashoka, D., BV, A. P. (2022). IMLAPC: interfused machine learning approach for prediction of crops. Revue d’Intelligence Artificielle, 36(1). https://doi.org/https://doi.org/10.18280/ria.360120
6. Bedaiwy, M. N. A. (2012). A simplified approach for determining the hydrometer’s dynamic settling depth in particle-size analysis. Catena, 97, 95–103. https://doi.org/https://doi.org/10.1016/j. catena.2012.05.010
7. Benabdelouahab, T., Lebrini, Y., Boudhar, A., Hadria, R., Htitiou, A., Lionboui, H. (2021). Monitoring spatial variability and trends of wheat grain yield over the main cereal regions in Morocco: a remote-based tool for planning and adjusting policies. Geocarto International, 36(20), 2303–2322. https://doi.org/https://doi.org/10.1080/10106049.2019.1695960
8. Blake, G., Hartge, K. (1986). Particle density. Methods of soil analysis: part 1 physical and mineralogical methods, 5, 377–382. https://doi.org/https://doi.org/10.2136/sssabookser5.1.2ed.c14
9. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5–32. https://doi.org/ https://doi.org/10.1023/A:1010933404324
10. Canadell, J., Jackson, R.B., Ehleringer, J., Mooney, H.A., Sala, O.E., Schulze, E.-D. (1996). Maximum rooting depth of vegetation types at the global scale. Oecologia, 108, 583–595. https://doi.org/https://doi.org/10.1007/BF00329030
11. Cheshire, L., & Woods, M. (2013). Globally engaged farmers as transnational actors: Navigating the landscape of agri-food globalization. Geoforum, 44, 232–242. https://doi.org/https://doi.org/10.1016/j.geoforum.2012.09.003
12. Cousin, I., Nicoullaud, B., Coutadeur, C. (2003). Influence of rock fragments on the water retention and water percolation in a calcareous soil. Catena, 53(2), 97–114. https://doi.org/https://doi.org/10.1016/S0341-8162(03)00037-7
13. Dedeoğlu, M., & Dengiz, O. (2019). Generating of land suitability index for wheat with hybrid system aproach using AHP and GIS. Computers and Electronics in Agriculture, 167, 105062. https://doi.org/ https://doi.org/10.1016/j.compag.2019.105062
14. Diaz-Gonzalez, F.A., Vuelvas, J., Correa, C.A., Vallejo, V.E., Patino, D. (2022). Machine learning and remote sensing techniques applied to estimate soil indicators – Review. Ecological Indicators, 135, 108517. https://doi.org/https://doi.org/10.1016/j. ecolind.2021.108517
15. El Baroudy, A. (2016). Mapping and evaluating land suitability using a GIS-based model. Catena, 140, 96–104. https://doi.org/https://doi.org/10.1016/j.catena.2015.12.010
16. Eyo, E., Abbey, S. (2022). Multiclass stand-alone and ensemble machine learning algorithms utilised to classify soils based on their physico-chemical characteristics. Journal of Rock Mechanics and Geotechnical Engineering, 14(2), 603–615. https://doi.org/https://doi.org/10.1016/j.jrmge.2021.08.011
17. Ganesan, M., Andavar, S., Raj, R.S.P. (2021). Prediction of land suitability for crop cultivation using classification techniques. Brazilian Archives of Biology and Technology, 64, e21200483. https://doi.org/https://doi.org/10.1590/1678-4324-2021200483
18. Geerts, S., Raes, D., Garcia, M., Del Castillo, C., Buytaert, W. (2006). Agro-climatic suitability mapping for crop production in the Bolivian Altiplano: A case study for quinoa. Agricultural and Forest Meteorology, 139(3–4), 399–412. https://doi.org/ https://doi.org/10.1016/j.agrformet.2006.08.018
19. Habibie, M.I., Noguchi, R., Shusuke, M., Ahamed, T. (2021). Land suitability analysis for maize production in Indonesia using satellite remote sensing and GIS-based multicriteria decision support system. GeoJournal, 86(2), 777–807. https://doi.org/ https://doi.org/10.1007/s10708-019-10091-5
20. Hamza, M., Anderson, W.K. (2005). Soil compaction in cropping systems: A review of the nature, causes and possible solutions. Soil and Tillage Research, 82(2), 121–145. https://doi.org/ https://doi.org/10.1016/j.still.2004.08.009
21. Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M.N., Geng, X., Bauer- Marschallinger, B. (2017). SoilGrids250m: Global gridded soil information based on machine learning. Plos one, 12(2), e0169748. https://doi.org/https://doi.org/10.1371/journal.pone.0169748
22. Huang, Z., Liu, Y., Tian, F.-P., Wu, G.-L. (2020). Soil water availability threshold indicator was determined by using plant physiological responses under drought conditions. Ecological Indicators, 118, 106740. https://doi.org/ https://doi.org/10.1016/j.ecolind.2020.106740
23. Ismaili, M., Krimissa, S., Namous, M., Htitiou, A., Abdelrahman, K., Fnais, M.S., Lhissou, R., Eloudi, H., Faouzi, E., Benabdelouahab, T. (2023). Assessment of Soil Suitability Using Machine Learning in Arid and Semi-Arid Regions. Agronomy, 13(1), 165. https://doi.org/https://doi.org/10.3390/agronomy13010165
24. Jackson, M.L. (2005). Soil chemical analysis: advanced course. UW-Madison Libraries parallel press.
25. Jayaraman, V., Sridevi, S., Monica, K., Lakshminarayanan, A. R. (2021). Predicting the Soil Suitability using Machine Learning Techniques. 2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON), https://doi.org/https://doi.org/10.1109/CENTCON52345.2021.9688283
26. Jones, J. (2018). Soil analysis handbook of reference methods. CRC press. https://doi.org/ https://doi.org/10.1201/9780203739433
27. Jonsson, P., Eklundh, L. (2002). Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing, 40(8), 1824–1832. https://doi.org/ https://doi.org/10.1109/TGRS.2002.802519
28. Junior, E.C., Gonçalves Jr, A.C., Seidel, E.P., Ziemer, G.L., Zimmermann, J., Oliveira, V.H.D. d., Schwantes, D., Zeni, C.D. (2020). Effects of liming on soil physical attributes: a review. Journal of Agricultural Science, 12(10), 278. https://doi.org/ https://doi.org/10.5539/jas.v12n10p278
29. Kang, E., Li, Y., Zhang, X., Yan, Z., Wu, H., Li, M., Yan, L., Zhang, K., Wang, J., Kang, X. (2021). Soil pH and nutrients shape the vertical distribution of microbial communities in an alpine wetland. Science of the total environment, 774, 145780. https://doi.org/https://doi.org/10.1016/j.scitotenv.2021.145780
30. Kim, K. (2021). Normalized class coherence change-based kNN for classification of imbalanced data. Pattern Recognition, 120, 108126. https://doi.org/https://doi.org/10.1016/j.patcog.2021.108126
31. Klute, A. (1986). Water retention: laboratory methods. Methods of soil analysis: part 1 physical and mineralogical methods, 5, 635–662. https://doi.org/ https://doi.org/10.2136/sssabookser5.1.2ed.c26
32. Li, Z., Fan, Z., Shen, S. (2018). Urban green space suitability evaluation based on the AHP-CV combined weight method: A case study of Fuping county, China. Sustainability, 10(8), 2656. https://doi.org/ https://doi.org/10.3390/su10082656
33. Ljubičić, N., Kostić, M., Oskar, M., Panić, M., Brdar, S., Lugonja, P., Knežević, M., Minić, V., Ivošević, B., Jevtić, R. (2018). Estimation of aboveground biomass and grain yield of winter wheat using NDVI measurements. Book of Proceedings, 9th International Scientific Agriculture Symposium Agrosym 2018, 4–7 October 2018, Jahorina, http:// fiver.ifvcns.rs/handle/123456789/4083
34. Luo, G., Li, L., Friman, V.-P., Guo, J., Guo, S., Shen, Q., Ling, N. (2018). Organic amendments increase crop yields by improving microbe-mediated soil functioning of agroecosystems: A meta-analysis. Soil Biology and Biochemistry, 124, 105–115. https://doi.org/ https://doi.org/10.1016/j.soilbio.2018.06.002
35. Mangan, P., Pandi, D., Haq, M.A., Sinha, A., Nagarajan, R., Dasani, T., Keshta, I., Alshehri, M. (2022). Analytic hierarchy process based land suitability for organic farming in the arid region. Sustainability, 14(8), 4542. https://doi.org/ https://doi.org/10.3390/ su14084542
36. Mbugua, J.K., Suksa-ngiam, W. (2018). Predicting suitable areas for growing cassava using remote sensing and machine learning techniques: A study in Nakhon-Phanom Thailand. Issues in Informing Science and Information Technology, 15, 43–56. https://doi.org/ https://doi.org/10.28945/4024
37. McBratney, A.B., Mendonça Santos, M.L., Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1), 3–52. https://doi.org/ https://doi.org/10.1016/S0016-7061(03)00223-4
38. Møller, A.B., Mulder, V.L., Heuvelink, G.B., Jacobsen, N.M., Greve, M.H. (2021). Can we use machine learning for agricultural land suitability assessment? Agronomy, 11(4), 703. https://doi.org/ https://doi.org/10.3390/agronomy11040703
39. Namous, M., Hssaisoune, M., Pradhan, B., Lee, C.- W., Alamri, A., Elaloui, A., Edahbi, M., Krimissa, S., Eloudi, H., Ouayah, M., Elhimer, H., & Tagma, T. (2021). Spatial prediction of groundwater potentiality in large semi-arid and karstic mountainous region using machine learning models. Water, 13(16). https://doi.org/ https://doi.org/10.3390/w13162273
40. Oertli, J. (1990). Limitations to the diagnostic information obtained from soil analyses. Fertilizer research, 26, 189–196. https://doi.org/ https://doi.org/10.1007/BF01048756
41. Özen, D., Kocakaya, A., Ozbeyaz, C. (2021). Estimating relationship between live body weight and type traits at weaning and six months of age in Bafra lambs using canonical correlation analysis. Journal of Animal and Plant Sciences-Japs, 31(2). https://doi.org/http://doi.org/10.36899/japs.2021.2.0226
42. Öztürk, M.M. (2017). Which type of metrics are useful to deal with class imbalance in software defect prediction? Information and Software Technology, 92, 17–29. https://doi.org/ https://doi.org/10.1016/j.infsof.2017.07.004
43. Pereira, P., Brevik, E.C., Muñoz-Rojas, M., Miller, B.A., Smetanova, A., Depellegrin, D., Misiune, I., Novara, A., Cerdà, A. (2017). Chapter 2 - Soil Mapping and Processes Modeling for Sustainable Land Management. In P. Pereira, E. C. Brevik, M. Muñoz- Rojas, & B. A. Miller (Eds.), Soil Mapping and Process Modeling for Sustainable Land Use Management (29–60). Elsevier. https://doi.org/ https://doi.org/10.1016/B978-0-12-805200-6.00002-5
44. Poesen, J., Nachtergaele, J., Verstraeten, G., Valentin, C. (2003). Gully erosion and environmental change: importance and research needs. Catena, 50(2–4), 91–133. https://doi.org/ https://doi.org/10.1016/S0341-8162(02)00143-1
45. Purnamasari, R.A., Ahamed, T., Noguchi, R. (2019). Land suitability assessment for cassava production in Indonesia using GIS, remote sensing and multi-criteria analysis. Asia-Pacific Journal of Regional Science, 3, 1–32. https://doi.org/https://doi.org/10.1007/s41685-018-0079-z
46. Qu, C., Li, P., Zhang, C. (2021). A spectral index for winter wheat mapping using multi-temporal Landsat NDVI data of key growth stages. ISPRS journal of photogrammetry and remote sensing, 175, 431–447. https://doi.org/ https://doi.org/10.1016/j.isprsjprs.2021.03.015
47. Radočaj, D., Jurišić, M., Gašparović, M., Plaščak, I., Antonić, O. (2021). Cropland suitability assessment using satellite-based biophysical vegetation properties and machine learning. Agronomy, 11(8), 1620. https://doi.org/ https://doi.org/10.3390/agronomy11081620
48. Raheem, A.M., Naser, I.J., Ibrahim, M.O., Omar, N.Q. (2023). Inverse distance weighted (IDW) and kriging approaches integrated with linear single and multi-regression models to assess particular physico-consolidation soil properties for Kirkuk city. Modeling Earth Systems and Environment, 9(4), 3999–4021. https://doi.org/ https://doi.org/10.1007/s40808-023-01730-5
49. Romano, G., Dal Sasso, P., Trisorio Liuzzi, G., Gentile, F. (2015). Multi-criteria decision analysis for land suitability mapping in a rural area of Southern Italy. Land use policy, 48, 131–143. https://doi.org/https://doi.org/10.1016/j.landusepol.2015.05.013
50. Romeijn, H., Faggian, R., Diogo, V., Sposito, V. (2016). Evaluation of deterministic and complex analytical hierarchy process methods for agricultural land suitability analysis in a changing climate. ISPRS International Journal of Geo-Information, 5(6). https://doi.org/https://doi.org/10.3390/ijgi5060099
51. Rossiter, D.G. (1996). A theoretical framework for land evaluation. Geoderma, 72(3– 4), 165–190. https://doi.org/https://doi.org/10.1016/0016-7061(96)00031-6
52. Sarkar, D., Saha, S., Maitra, M., Mondal, P. (2021). Site suitability for Aromatic Rice cultivation by integrating Geo-spatial and Machine learning algorithms in Kaliyaganj C.D. block, India. Artificial Intelligence in Geosciences, 2, 179–191. https://doi.org/https://doi.org/10.1016/j.aiig.2022.03.001
53. Shrivastava, P., Kumar, R. (2015). Soil salinity: A serious environmental issue and plant growth promoting bacteria as one of the tools for its alleviation. Saudi journal of biological sciences, 22(2), 123–131. https://doi.org/https://doi.org/10.1016/j.sjbs.2014.12.001
54. Talukdar, S., Naikoo, M.W., Mallick, J., Praveen, B., Sharma, P., Islam, A.R.M.T., Pal, S., Rahman, A. (2022). Coupling geographic information system integrated fuzzy logic-analytical hierarchy process with global and machine learning based sensitivity analysis for agricultural suitability mapping. Agricultural systems, 196, 103343. https://doi.org/ https://doi.org/10.1016/j.agsy.2021.103343
55. Vapnik, V. (2013). The nature of statistical learning theory. Springer science & business media.
56. Velmurugan, A., Swarnam, T.P., Ambast, S.K., Kumar, N. (2016). Managing waterlogging and soil salinity with a permanent raised bed and furrow system in coastal lowlands of humid tropics. Agricultural Water Management, 168, 56–67. https://doi.org/https://doi.org/10.1016/j.agwat.2016.01.020
57. Zhong, L., Hu, L., Zhou, H., Tao, X. (2019). Deep learning based winter wheat mapping using statistical data as ground references in Kansas and northern Texas, US. Remote Sensing of Environment, 233, 111411. https://doi.org/https://doi.org/10.1016/j.rse.2019.111411

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-3eff4d83-f906-4c5d-b1e5-7bbb46fbb47c