19619

Meteoroloji ve Tarım İlacı Kullanım Verilerinin Makine Öğrenmesi Yöntemlerine Entegre Edilmesi Yoluyla Tarımsal Üretim Tahmini: Türkiye’deki Başlıca Mahsuller İçin Bir Uygulama

En önemli ve hayati insan faaliyetlerden biri olarak tarım, küresel, yerel ve çevresel sorunlara karşı oldukça savunmasızdır. Bu kırılganlık COVID-19 pandemisinin ilk aşamalarında da görülmüştür. Bu bağlamda, söz konusu durumların tarımsal ürünlerin talep ve fiyatlama dinamikleri üzerinde önemli etkilerinin olduğu söylenebilmektedir. Yine de tarımsal faaliyetlerin temel amacı, mahsul verimi ve üretimini iyileştirmek olduğu ifade edilebilir. Türkiye'nin hızla değişen koşullarla başa çıkabilmesi için, 11. Kalkınma Planının da ana hedeflerinden biri olarak belirtilen veriye dayalı tarımsal bilgi sistemlerini geliştirmeye devam etmesi gerekmektedir. Dolayısıyla doğru üretim miktarı tahmini, kritik bir görev olarak öne çıkmaktadır. Bu doğrultuda, 1990-2019 dönemi için meteorolojik parametreler, tarım ilacı kullanımı ve rekolteye dayalı veri setlerini kullanarak, Türkiye'deki dokuz ana mahsulün üretim miktarı tahmininde makine öğrenmesi yöntemlerinin geçerliliğinin değerlendirilmesi, bu çalışmanın temel amacı olarak ifade edilebilir. Eğitim aşamasından sonra tüm modellerle üretim miktarı tahmini yapılmış, elde edilen sonuçlar gerçek değerlerle karşılaştırılmıştır. Sonuçlara göre Karar Ağacı Regresyon (KAR) ve Rastgele Orman Regresyon (ROR) yöntemleriyle, bilhassa buğday, arpa ve mısır için başarılı tahminler alınmış, Destek Vektör Regresyon (DVR) yönteminin ise tutarsız tahminler verdiği görülmüştür.

Crop Yield Prediction by Integrating Meteorological and Pesticides Use Data with Machine Learning Methods: An Application for Major Crops in Turkey

Agriculture, as one of the most important and vital human activity, is highly vulnerable to global, local and environmental issues. This fragility also surfaced in the initial stages of the COVID-19 pandemic. Accordingly, such matters are considered to have dramatic impacts on demand and pricing dynamics of agricultural products. Nonetheless, improving crop yield and its estimation is the fundamental goal of agricultural activities. To cope with the rapidly changing circumstances, Turkey needs to keep developing data-based agricultural information systems which is also stated as one of the main objectives of the 11th development plan. Therefore, accurate crop yield prediction appears to be a critical task. In this context, using meteorological parameters, pesticides use and crop yield values during 1990-2019, evaluation of machine learning regression methods in the yield prediction of nine major crops in Turkey can be stated as the main aim of this research. After the training, all models are used to predict crop yields and acquired values were compared with actual figures. The results showed that successful predictions were obtained by using the Decision Tree Regression (DTR) and Random Forest Regression (RFR) especially for wheat, barley and maize yields; however, Support Vector Regression (SVR) showed inconsistent predictions.

PDF

___

Ahmad, I., Saeed, U., Fahad, M., Ullah, A., Rahman, M.H., Ahmad, A. and Judge, J. (2018). Yield forecasting of spring maize using remote sensing and crop modeling in Faisalabad-Punjab Pakistan. Journal of the Indian Society of Remote Sensing, 46, 1701-1711. https://doi.org/10.1007/s12524- 018-0825-8
Alpaydın, E. (2004). Introduction to machine learning. Cambridge: The MIT Press.
Alston, D.G., Schmitt, D.P., Bradley, J.R. and Coble, H. (1993). Multiple pest interactions in soybean: Effects on heterodera glycines egg populations and crop yield. Journal of Nematology, 25(1), 42- 49. Retrieved from https://www.ncbi.nlm.nih.gov/
Araújo, S.O., Peres, R.S., Barata, J., Lidon, F. and Ramalho, J.C. (2021). Characterising the agriculture 4.0 landscape—emerging trends, challenges and opportunities. Agronomy, 11(4), 667-703. https://doi.org/10.3390/agronomy11040667
Bali, N. and Singla, A. (2022). Emerging trends in machine learning to predict crop yield and study its influential factors: A survey. Archives of Computational Methods in Engineering, 95, 95-112. https://doi.org/10.1007/s11831-021-09569-8
Basak, D., Pal, S. and Patranabis, D.C. (2007). Support vector regression. Neural Information Processing, 11(10), 203-224. Retrieved from https://citeseerx.ist.psu.edu/
Başakın, E.E., Ekmekcioglu, Ö., Özger, M. and Çelik, A. (2020). Prediction of Turkey wheat yield by wavelet fuzzy time series and gray prediction methods. Turkish Journal of Agricultural Research, 7(3), 246-252. https://doi.org/10.19159/tutad.685342
Baştürk, M.Ö., Turgut, K. and Hocaoğlu, A.K. (2021). Görüntü işleme tabanlı elma ağacinda rekolte tahmini. Paper presented at the Union Radio-Scientifique Internationale. Kocaeli, Turkey. Retrieved from http://ursitr2021.gtu.edu.tr/MCMSR/papers/URSI-TR_2020_paper_84.pdf
Benos, L., Tagarakis, A.C., Dolias, G., Berruto, R., Kateris D. and Bochtis, D. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), 3758-3813. https://doi.org/10.3390/s21113758
Bregaglio, S., Fischer K., Ginaldi, F., Valeriano, T. and Giustarini, L. (2021). The HADES yield prediction system–a case study on the Turkish hazelnut sector. Frontiers in Plant Science, 12, 1-14. https://doi.org/10.3389/fpls.2021.665471
Charoen-Ung, P. and Mittrapiyanuruk, P. (2018). Sugarcane yield grade prediction using random forest with forward feature selection and hyper-parameter tuning. In H. Unger, S. Sodsee and P. Meesad (Eds), Recent Advances in Information and Communication Technology 2018 (pp. 33-42). Paper Presented at International Conference on Computing and Information Technology, Cham: Springer. https://doi.org/10.1007/978-3-319-93692-5_4
Chen, P., Xie, G., Liu, H., Liang, L., Gao H., Wang, D. and Ji, W.J. (2020). Online output estimation for multimode process with dynamic time-delay. Paper presented at the 39th Chinese Control Conference (CCC). Shenyang, China. doi:10.23919/CCC50068.2020.9189279
Chlingaryan, A., Sukkarieh, S. and Whelan, B. (2018). Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61-69. https://doi.org/10.1016/j.compag.2018.05.012
Dang, C., Liu, Y., Yue, H., Qian, J. and Zhu, R. (2021). Autumn crop yield prediction using data-driven approaches: Support vector machines, random forest, and deep neural network methods. Canadian Journal of Remote Sensing, 47(2), 162-181. https://doi.org/10.1080/07038992.2020.1833186
Ercan, Ş., Öztep, R., Güler, D. and Saner, G. (2019). Tarım 4.0 ve Türkiye'de uygulanabilirliğinin değerlendirilmesi. Tarım Ekonomisi Dergisi, 25(2), 259-265. https://doi.org/10.24181/tarekoder.650762
Everingham, Y., Sexton, J., Skocaj, D. and Inman-Bamber, G. (2016). Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development, 36(27), 1-9. https://doi.org/10.1007/s13593-016-0364-z
FAO. (2021). The state of food and agriculture 2021: Making agrifood systems more resilient to shocks and stresses. Rome: FAO. https://doi.org/10.4060/cb4476en
Filippi, P., Jones, E.J., Wimalathunge, N.S., Somarathna, P.D., Pozza, L.E., Ugbaje, S.U., . . . and Bishop, T.F. (2019). An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precision Agriculture, 20, 1015-1029. https://doi.org/10.1007/s11119-018- 09628-4
França, T., Martins, A., Braga, B. and Ayala, H.V. (2022). Feature engineering to cope with noisy data in sparse identification. Expert Systems with Applications, 188, 115995. https://doi.org/10.1016/j.eswa.2021.115995
Gandhi, N., Armstrong L.J., Petkar O. and Tripath, A.K. (2016). Rice crop yield prediction in India using support vector machines. Paper presented at the 13th International Joint Conference on Computer Science and Software Engineering (JCSSE). Khon Kaen, Thailand. doi:10.1109/JCSSE.2016.7748856
Gopal, P.M. and Bhargavi, R. (2019a). Performance evaluation of best feature subsets for crop yield prediction using machine learning algorithms. Applied Artificial Intelligence, 33(7), 621-642. https://doi.org/10.1080/08839514.2019.1592343
Gopal, P.M. and Bhargavi, R. (2019b). A novel approach for efficient crop yield prediction. Computers and Electronics in Agriculture, 165, 104968. https://doi.org/10.1016/j.compag.2019.104968
Jeong, J.H., Resop, J.P., Mueller, N.D., Fleisher, D.H., Yun, K., Butler, E.E., . . . and Kim, S.–H. (2016). Random forests for global and regional crop yield predictions. PLoS ONE, 11(6), 1-15. https://doi.org/10.1371/journal.pone.0156571
Kang, Y., Ozdogan, M., Zhu, X., Ye, Z., Hain, C. and Anderson, M. (2020). Comparative assessment of environmental variables and machine learning algorithms for maize yield prediction in the US midwest. Environmental Research Letters, 15, 064005. https://doi.org/10.1088/1748-9326/ab7df9
Kawasaki, K. and Lichtenberg, E. (2015). Quality versus quantity effects of pesticides: Joint estimation of quality grade and crop yield. Paper presented at the Agricultural and Applied Economics Association (AAEA) Conferences. San Francisco, USA. doi:10.22004/ag.econ.204848
Kaya, Y. and Polat, N. (2021). Wheat yield estimation using vegetation indices. Dicle Üniversitesi Mühendislik Dergisi, 12(1), 99-110. https://doi.org/10.24012/dumf.860325
Khaki, S. and Wang, L. (2019). Crop yield prediction using deep neural networks. Frontiers in Plant Science, 10, 621. https://doi.org/10.3389/fpls.2019.00621
Khanal, S., Fulton, J., Klopfenstein A., Douridas, N. and Shearer, S. (2018). Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Computers and Electronics in Agriculture, 153, 213-225. https://doi.org/10.1016/j.compag.2018.07.016
Khosla, E., Dharavath, R. and Priya, R. (2020). Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression. Environment, Development and Sustainability, 22, 5687-5708. https://doi.org/10.1007/s10668-019-00445-x
Kılavuz, E. and Erdem, İ. (2019). Agriculture 4.0 applications in the world and transformation of Turkish agriculture. Social Sciences, 14(4), 133-157. http://dx.doi.org/10.12739/NWSA.2019.14.4.3C0189
Kırmıkıl, M. and Ertaş, B. (2020). A sustainable future with agriculture 4.0. Icontech International Journal, 4(1), 1-12. https://doi.org/10.46291/ICONTECHvol4iss1pp1-12
Klompenburg, T.V., Kassahun, A. and Catal, C. (2020). Crop yield prediction using machine learning: A systematic literature review. Computers and Electronics in Agriculture, 17, 105709. https://doi.org/10.1016/j.compag.2020.105709
Lamichhane, J.R. (2017). Pesticide use and risk reduction in European farming systems with IPM: An introduction to the special issue. Crop Protection, 97, 1-6. doi:10.1016/j.cropro.2017.01.017
Leo, S., Migliorati, M.D. and Grace, P.R. (2020). Predicting within-field cotton yields using publicly available datasets and machine learning. Agronomy Journal, 113(2), 1150-1163. https://doi.org/10.1002/agj2.20543
Liakos, K.G., Busato, P., Moshou, D., Pearson, S. and Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674-2703. https://doi.org/10.3390/s18082674
Lin, A.Y., Zhang, M. and Selpi, S. (2018). Using scaling methods to improve support vector regression’s performance for travel time and traffic volume predictions. In I. Rojas, H. Pomares and O. Valenzuela (Eds.), Time series analysis and forecasting ITISE 2017 contributions to statistics (pp. 115-127). Paper Presented at International Work-Conference on Time Series Analysis, Cham: Springer. https://doi.org/10.1007/978-3-319-96944-2_8
Lischeid, G., Webber, H., Sommer, M., Nendel, C. and Ewert, F. (2022). Machine learning in crop yield modelling: A powerful tool, but no surrogate for science. Agricultural and Forest Meteorology, 312, 108698. https://doi.org/10.1016/j.agrformet.2021.108698
Liu, Y., Miller, E. and Habib, K.N. (2021). Detecting transportation modes using smartphone data and GIS information: Evaluating alternative algorithms for an integrated smartphone-based travel diary imputation. Transportation Letters, 1958591. https://doi.org/10.1080/19427867.2021.1958591
Lobell, D.B. and Burke, M.B. (2008). Why are agricultural impacts of climate change so uncertain? The importance of temperature relative to precipitation. Environmental Research Letters, 3(3), 034007. https://doi.org/10.1088/1748-9326/3/3/034007
McQueen, R.J., Gamer, S.R., Nevill-Manning, C.G. and Witten, I.H. (1995). Applying machine learning to agricultural data. Computers and Electronics in Agriculture, 12, 275-293. https://doi.org/10.1016/0168-1699(95)98601-9
Millán-Castillo, R.S., Morgado, E. and Goya-Esteban, R. (2020). On the use of decision tree regression for predicting vibration frequency response of handheld probes. IEEE Sensors Journal, 20(8), 4120 - 4130. https://doi.org/10.1109/JSEN.2019.2962497
OECD/FAO. (2020). OECD-FAO agricultural outlook 2020-2029. Retrieved from https://doi.org/10.1787/1112c23b-en
Oerke, E.-C. (2006). Crop losses to pests. The Journal of Agricultural Science, 144(1), 31-43. https://doi.org/10.1017/S0021859605005708
Ozdogan, B., Gacar, A. and Aktas, H. (2017). Digital agriculture practices in the context of agriculture 4.0. Pressacademia, 4, 184-191. https://doi.org/10.17261/Pressacademia.2017.448
Pant, J., Pant, R., Singh, M.K., Singh, D.P. and Pant, H. (2021). Analysis of agricultural crop yield prediction using statistical techniques of machine learning. Materials Today: Proceedings, 46, 10922-10926. https://doi.org/10.1016/j.matpr.2021.01.948
Patrício, D.I. and Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture, 153, 69-81. https://doi.org/10.1016/j.compag.2018.08.001
Paudel, D., Boogaard, H. Wit, A.D., Janssen, S., Osinga, S., Pylianidis, C. and Athanasiadis, I.N. (2021). Machine learning for large-scale crop yield forecasting. Agricultural Systems, 187, 103016. https://doi.org/10.1016/j.agsy.2020.103016
Paudel, D., Boogaard, H., Wit, A.D, Velde, M.D., Claverie, M., . . . Athanasiadis, I.N. (2022). Machine learning for regional crop yield forecasting in Europe. Field Crops Research, 276, 108377. https://doi.org/10.1016/j.fcr.2021.108377
Pekel, E. (2020). Estimation of soil moisture using decision tree regression. Theoretical and Applied Climatology, 139, 1111–1119. https://doi.org/10.1007/s00704-019-03048-8
PSB. (2019). Eleventh development plan (2019-2023). Retrieved from https://www.sbb.gov.tr/
Rahman, M.M., Haq N. and Rahman, R.M. (2014). Machine learning facilitated rice prediction in Bangladesh. Paper presented at the 2014 Annual Global Online Conference on Information and Computer Technology. Louisville, USA. Retrieved from https://ieeexplore.ieee.org/
Rashid, M., Bari, B.S., Yusup, Y., Kamaruddin, M.A. and Khan, N. (2021). A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction. IEEE Access, 9, 63406-63439. https://doi.org/10.1109/ACCESS.2021.3075159
Schwalbert, R.A., Amado, T., Corassa, G., Pott, L.P., Prasad, P. and Ciampitti, I.A. (2020). Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil. Agricultural and Forest Meteorology, 284, 107886. https://doi.org/10.1016/j.agrformet.2019.107886
Scornet, E., Biau, G. and Vert, J.-P. (2015). Consistency of random forests. The Annals of Statistics, 43(4), 1716-1741. https://doi.org/10.1214/15-AOS1321
Shah, A., Dubey, A., Hemnani, V., Gala, D. and Kalbande, D.R. (2018). Smart farming system: Crop yield prediction using regression techniques. In H. Vasudevan, A. Deshmukh and K. Ray (Eds.), Proceedings of International Conference on Wireless Communication (pp. 49-56). Paper Presented at the International Conference on Wireless Communication, Singapore: Springer. https://doi.org/10.1007/978-981-10-8339-6_6
Shi, X., An, X., Zhao, Q., Liu, H., Xia, L., Sun, X. and Guo, Y. (2019). State-of-the-art internet of things in protected agriculture. Sensors, 19(8), 1833. https://doi.org/10.3390/s19081833
Shook, J., Gangopadhyay, T., Wu, L., Ganapathysubramanian, B., Sarkar, S. and Singh, A.K. (2021). Crop yield prediction integrating genotype and weather variables using deep learning. PLoS ONE, 16(6), e0252402. https://doi.org/10.1371/journal.pone.0252402
Şimşek, O., Mermer, A., Yıldız, H., Özaydın, K.A. and Çakmak, B. (2007). Estimation of wheat yield for Turkey using AgroMetShell model. Journal of Agricultural Sciences, 13(3), 299-307. Retrieved from https://dergipark.org.tr/en/pub/ankutbd/
Tang, S., Zhu, Q., Zhou, X., Liu, S. and Wu, M. (2002). A conception of digital agriculture. Paper presented at the International Geoscience and Remote Sensing Symposium. Toronto, Canada. https://doi.org/10.1109/IGARSS.2002.1026858
Tauger, M.B. (2011). Agriculture in world history. Oxfordshire: Routledge.
Toscano, N.C., Sances, F.V., Johnson, M.W. and Lapre, L.F. (1982). Effect of various pesticides on lettuce physiology and yield. Journal of Economic Entomology, 75(4), 738-741. https://doi.org/10.1093/jee/75.4.738
Trnka, M., Olesen, J.E., Kersebaum, K.C., Rötter, R.P., Brázdil, R., Eitzinger, J., . . . Semerádová, D. (2016). Changing regional weather-crop yield relationships across Europe between 1901 and 2012. Climate Research, 70, 195-214. https://doi.org/10.3354/cr01426
Vaid, K. and Ghose, U. (2020). Predictive analysis of manpower requirements in scrum projects using regression technique. Procedia Computer Science, 173, 335–344. https://doi.org/10.1016/j.procs.2020.06.039
Vanli, Ö., Ahmad, I. and Ustundag, B.B. (2020). Area estimation and yield forecasting of wheat in southeastern Turkey using a machine learning approach. Journal of the Indian Society of Remote Sensing, 48(21), 1757-1766. https://doi.org/10.1007/s12524-020-01196-3
Varjovi, M.H. and Talu, M.F. (2016). Kayısı için otomatik rekolte tahmin sistemi. Paper presented at International Conference on Artificial Intelligence and Data Processing. Malatya, Turkey. Retrieved from https:// http://idap.inonu.edu.tr/
Washuck, N., Hanson, M. and Prosser, R. (2022). Yield to the data: Some perspective on crop productivity and pesticides. Pest Management Science, 78(5), 1765-1771. https://doi.org/10.1002/ps.6782
Xie, S., Feng, H., Yang, F., Zhao, Z., Hu, X., Wei, C., . . . Geng, Y. (2019). Does dual reduction in chemical fertilizer and pesticides improve nutrient loss and tea yield and quality? A pilot study in a green tea garden in Shaoxing, Zhejiang Province, China. Environmental Science and Pollution Research, 26, 2464–2476. https://doi.org/10.1007/s11356-018-3732-1
Xu, M., Watanachaturaporn, P., Varshney, P.K. and Arora, M.K. (2005). Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, 97, 322-336. https://doi.org/10.1016/j.rse.2005.05.008
Xu, X., Gao, P., Zhu, X., Guo, W., Ding, J., Li, C., . . . Wu, X. (2019). Design of an integrated climatic assessment indicator (ICAI) for wheat production: A case study in Jiangsu Province, China. Ecological Indicators, 101, 943-953. https://doi.org/10.1016/j.ecolind.2019.01.059
Zambon, I., Cecchini, M., Egidi, G., Saporito, M.G. and Colantoni, A. (2019). Revolution 4.0: Industry vs. agriculture in a future development for SMEs. Processes, 7(36), 1-16. https://doi.org/10.3390/pr7010036
Zarei, A.R., Mahmoudi, M.R., Shabani, A. and Achite, M. (2021). Determination of the most important meteorological parameters affecting the yield and biomass of barley and winter wheat using the random forest algorithm. Paddy and Water Environment, 19, 199-216. https://doi.org/10.1007/s10333-020-00832-5
Zuo, X., Guo, H., Shi, S. and Zhang, X. (2020). Comparison of six machine learning methods for estimating PM2.5 concentration using the himawari-8 aerosol optical depth. Journal of the Indian Society of Remote Sensing, 48(9), 1277–1287. https://doi.org/10.1007/s12524-020-01154-z