Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, Cilt: 38 Sayı: 3, 445 - 462, 16.12.2024

Öz

Kaynakça

  • Abbott P, Hurt C, Tyner E (2011). What’s driving food prices in 2011. Farm Foundation. Oak Brook, IL, USA.
  • Amankulova K, Farmonov N, Mukhtorov U, Mucsi L (2023). Sunflower crop yield prediction by advanced statistical modeling using satellite-derived vegetation indices and crop phenology. Geocarto International 38(1):2197509.
  • Aouad M, Hajj H, Shaban K, Jabr RA, El-Hajj W (2022). A CNN-Sequence-to-sequence network with attention for residential short-term load forecasting. Electr. Power Syst. Res 211:108152.
  • Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D (2021). Machine learning in agriculture: A comprehensive updated review. Sensors 21(11): 3758. https://doi.org/10.3390/s21113758.
  • Breiman L (2001). Random Forests. Machine Learning 45: 5–32.
  • Burke M, Lobel D (2017). Satellite-based assessment of yield and its determinants in smallholder african systems. Proceedings of the National Academy of Sciences 114(9):2189-2194.
  • Byerlee D, de Janvy A, Sadoulet E (2009). Agriculture for development: toward a new paradigm. Annual Review of Resource Economics 1:15-31.
  • Călin AD, Mureşan H-B, Coroiu AM (2022). Feasibility of using machine learning algorithms for yield prediction of corn and sunflower crops based on seeding date. Studia Univ. Babes–Bolyai, Informatica LXVII( 2) https://doi.org/10.24193/subbi.2022.2.02.
  • Ceyhan E, Önder M, Öztürk Ö, Harmankaya M, Hamurcu M, Gezgin S (2008). Effects of application boron on yields, yield component and oil content of sunflower in boron-deficient calcareous soils. African Journal of Biotechnology 7(16): 2854-2861.
  • Chung, W. H., Gu, Y. H., & Yoo, S. J. (2022). District heater load forecasting based on machine learning and parallel CNN-LSTM attention. Energy 246, 123350.
  • Cortes C, Vapnik V (1995). Support vector networks. Machine Learning 20:273-297.
  • Cui, M. (2022). District heating load prediction algorithm based on bidirectional long short-term memory network model. Energy 254: 124283.
  • Dahikar SS, Rode S (2014). Agricultural crop yield prediction using artificial neural network approach. International Journal of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering 2(1): 683-686
  • Debaeke P, Attia F, Champolivier L, Dejoux J-F, Micheneau A, Al Bitar A, Tr ́epos R (2023). Forecasting sunflower grain yield using remote sensing data and statistical models. European Journal of Agronomy 142 (2023): 126677.
  • Everingham Y, Sexton J, Skocaj D, Inman-Bamber G (2016). Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development 36(2): 27.
  • Fukuda S, Spreer W, Yasunaga E, Yuge K, Sardsud V, Müller J (2013). Random forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes. Agric Water Manag 116: 142-150. https://doi.org/10.1016/j.agwat.2012.07.003
  • Gandhi N, Armstrong LJ, Petkar O, Tripathy AK (2016). Rice crop yield prediction in India using support vector machines. IEEE Xplorer, 13th International Joint Conference on Computer Science and Software Engineering (JCSSE).
  • Gonzalez-Sanchez A, Frausto-Solis J, Ojeda-Bustamante W (2014). Predictive ability of machine learning methods for massive crop yield prediction. Spanish Journal of Agricultural Research 12(2): 313-328.
  • Hadjout D, Torres J, Troncoso A, Sebaa A, Martínez-Álvarez F (2022). Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market. Energy 243: 123060.
  • Iniyan S, Varmaa VA, Naidu CT (2023). Crop yield prediction using machine learning techniques. Advances in Engineering Software 175: 103326.
  • Jain N, Kumar A, Garud S, Pradhan V, Kulkarni P (2017). Crop selection method based on various environmental factors using machine learning. International Research Journal of Engineering and Technology 4(2): 56-72.
  • Jiang D, Yang X, Clinton N, Wang N (2004). An artificial neural network model for estimating crop yield using remotely sensed information. Int. J. Remote Sensing 25: 1723-1732.
  • Kalichkin VK, Alsova OK, Maksimovich KY (2021). Application of the decision tree method for predicting the yield of spring wheat. AGRITECH-V-2021 IOP Conf. Series: Earth and Environmental Science 839: 032042 https://doi.org/10.1088/1755-1315/839/3/032042
  • Kaul M, Hill RL, Walthall C (2005). Artificial neural networks for corn and soybean yield prediction. Agric. Syst 85: 1–18.
  • Kayad A, Sozzi M, Gatto S, Marinello F, Pirotti F (2019). Monitoring within-field variability of corn yield using Sentinel-2 and machine learning techniques. Remote Sensing 11(23): 2873.
  • Khaki S, Wang L (2019). Crop yield prediction using deep neural networks. Frontiers in Plant Science 10: 452963.
  • Laxmi RR, Kumar A (2011). Weather based forecasting model for crops yield using neural network approach. Statistics and Applications 9(1), 55-69.
  • Leroux L, Castets M, Baron C, Escorihuela MJ, Bégué A, Seen DL (2019). Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices. European Journal of Agronomy 108: 11-26.
  • Liu J, Goering CE, Tian L (2001). Neural network for setting target corn yields. T ASAE 44(3): 705-713.
  • Mishra S, Mishra D, Santra G (2016). Applications of machine learning techniques in agricultural crop production: A review paper. Indian Journal of Science and Technology 9(38).
  • Mok H-F, Dassanayake KB, Hepworth G, Hamilton AJ (2014). Field comparison and crop production modeling of sweet corn and silage maize (Zea mays L.) with treated urban wastewater and freshwater. Irrigation Science 32(5): 351–368. https://doi.org/10.1007/s00271-014-0434-4.
  • Mourtzinis S, Esker PD, Specht JE, Conley SP (2021). Advancing agricultural research using machine learning algorithms. Scientific Reports 11(1): 1–7.
  • Narin OG, Abdikan S (2022). Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images, Geocarto International 37(5): 1378-1392, https://doi.org/10.1080/10106049.2020.1765886.
  • Önder M, Öztürk Ö, Ceyhan E (2001). Yağlık ayçiçeği çeşitlerinin verim ve bazı verim unsurlarının belirlenmesi. S.Ü. Ziraat Fakültesi Dergisi 15 (28): 136-146.
  • Palatnik RR, Roson R (2012). Climate change and agriculture in computable general equilibrium models: alternative modeling strategies and data needs. Climatic Change 112: 1085–1100.
  • Paudel D, Boogaard H, de Wit A, Janssen S, Osinga S, Pylianidis C, Athanasiadis I (2021). Machine learning for large-scale crop yield forecasting. Agricultural Systems 187(1).
  • Priyadharshini K, Prabavathi R, Devi VB, Subha P, Saranya SM, Kiruthika K (2022). An enhanced approach for crop yield prediction system using linear support vector machine model. In 2022 IEEE International Conference on Communication, Computing, and Internet of Things (IC3IoT) :1-5.
  • Putt ED (1977). Early history of sunflowers. In: A.A. Schneiter (ed). Sunflower Technology and Production. ASACSSA and SSSA Madison, WI. p. 1-19.
  • Republic of Turkey Ministry of Agriculture and Forestry, Sunflower Bulletin, 20 May, 2022
  • Rokach L, Maimon O (2014). Data mining with decision tree; series in machine perception and artificial intelligence. World Scientific 81: 61-62.
  • Salam A, El Hibaoui A. (2021) Energy consumption prediction model with deep inception residual network inspiration and LSTM. Math. Comput. Simul 190: 97–109.
  • Singh R, Singh G (2017). Wheat crop yield assessment using decision tree algorithms. International Journal of Advanced Research in Computer Science 8(5):1809-1817.
  • Tang XY, Yang WW, Liu Z, Li JC, Ma X (2024). Deep learning performance prediction for solar-thermal-driven hydrogen production membrane reactor via bayesian optimized LSTM. International Journal of Hydrogen Energy 82, 1402-1412.
  • URL1 https://biruni.tuik.gov.tr (access date: 06.06.2023).
  • URL2 https://data.tuik.gov.tr (access date: 04.08.2023).
  • URL3 Turkish State Meteorological Service, https://www.mgm.gov.tr (access date: 23.09.2023).
  • USDA (2020). U.S. Department of Agriculture, Oil crops yearbook, https://www.ers. usda.gov/data-products/oil-crops-yearbook/oil-Crops-Yearbook/ (access date:02.10.2020)
  • Xu L, Hou L, Zhu Z, Li Y, Liu J, Lei T, Wu X (2021). Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm. Energy 222: 119955.
  • Yan, X., Ji, X., Meng, Q., Sun, H., & Lei, Y. (2024). A hybrid prediction model of improved bidirectional long short-term memory network for cooling load based on PCANet and attention mechanism. Energy 292: 130388.

Sunflower Crop Yield Prediction Using Machine Learning Methods

Yıl 2024, Cilt: 38 Sayı: 3, 445 - 462, 16.12.2024

Öz

Sunflower, one of the most important crops, is produced in many countries to meet especially for edible oil demand. Since the sunflower plant is affected by many factors, such as the amount of rain and air temperature, the yield changes from year to year, which has adverse effects on the balance between demand and supply. Because of the product produced in many countries is not enough; it has to be imported. Turkey is one of the world’s leading sunflower importers. The yield must be accurately estimated for the imported quantity to be correct. Importing in large quantities causes inventories, while small quantities cause the sunflower oil demand to not be met. It is used methods such as the direct method, simulation, and remote sensing to estimate sunflower yield. However, these methods have some shortcomings. In this article, machine learning methods, such as Artificial Neural Network, Decision Tree, Support Vector Machine and Random Forest, are used for production prediction. In order to increase the effectiveness of the methods, the values of the hyperparameters are determined by Halving Grid Search method that is tuning method. The methods were implemented in Edirne, which is among the province with the highest sunflower yield in Turkey. The results were evaluated with ANOVA method and performance evaluation metrics, RMSE, RRSE, AE, and R. Decision Tree method, providing the prediction with the lowest error, is determined a suitable method for sunflower yield prediction and then accurate buying decision making.

Kaynakça

  • Abbott P, Hurt C, Tyner E (2011). What’s driving food prices in 2011. Farm Foundation. Oak Brook, IL, USA.
  • Amankulova K, Farmonov N, Mukhtorov U, Mucsi L (2023). Sunflower crop yield prediction by advanced statistical modeling using satellite-derived vegetation indices and crop phenology. Geocarto International 38(1):2197509.
  • Aouad M, Hajj H, Shaban K, Jabr RA, El-Hajj W (2022). A CNN-Sequence-to-sequence network with attention for residential short-term load forecasting. Electr. Power Syst. Res 211:108152.
  • Benos L, Tagarakis AC, Dolias G, Berruto R, Kateris D, Bochtis D (2021). Machine learning in agriculture: A comprehensive updated review. Sensors 21(11): 3758. https://doi.org/10.3390/s21113758.
  • Breiman L (2001). Random Forests. Machine Learning 45: 5–32.
  • Burke M, Lobel D (2017). Satellite-based assessment of yield and its determinants in smallholder african systems. Proceedings of the National Academy of Sciences 114(9):2189-2194.
  • Byerlee D, de Janvy A, Sadoulet E (2009). Agriculture for development: toward a new paradigm. Annual Review of Resource Economics 1:15-31.
  • Călin AD, Mureşan H-B, Coroiu AM (2022). Feasibility of using machine learning algorithms for yield prediction of corn and sunflower crops based on seeding date. Studia Univ. Babes–Bolyai, Informatica LXVII( 2) https://doi.org/10.24193/subbi.2022.2.02.
  • Ceyhan E, Önder M, Öztürk Ö, Harmankaya M, Hamurcu M, Gezgin S (2008). Effects of application boron on yields, yield component and oil content of sunflower in boron-deficient calcareous soils. African Journal of Biotechnology 7(16): 2854-2861.
  • Chung, W. H., Gu, Y. H., & Yoo, S. J. (2022). District heater load forecasting based on machine learning and parallel CNN-LSTM attention. Energy 246, 123350.
  • Cortes C, Vapnik V (1995). Support vector networks. Machine Learning 20:273-297.
  • Cui, M. (2022). District heating load prediction algorithm based on bidirectional long short-term memory network model. Energy 254: 124283.
  • Dahikar SS, Rode S (2014). Agricultural crop yield prediction using artificial neural network approach. International Journal of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering 2(1): 683-686
  • Debaeke P, Attia F, Champolivier L, Dejoux J-F, Micheneau A, Al Bitar A, Tr ́epos R (2023). Forecasting sunflower grain yield using remote sensing data and statistical models. European Journal of Agronomy 142 (2023): 126677.
  • Everingham Y, Sexton J, Skocaj D, Inman-Bamber G (2016). Accurate prediction of sugarcane yield using a random forest algorithm. Agronomy for Sustainable Development 36(2): 27.
  • Fukuda S, Spreer W, Yasunaga E, Yuge K, Sardsud V, Müller J (2013). Random forests modelling for the estimation of mango (Mangifera indica L. cv. Chok Anan) fruit yields under different irrigation regimes. Agric Water Manag 116: 142-150. https://doi.org/10.1016/j.agwat.2012.07.003
  • Gandhi N, Armstrong LJ, Petkar O, Tripathy AK (2016). Rice crop yield prediction in India using support vector machines. IEEE Xplorer, 13th International Joint Conference on Computer Science and Software Engineering (JCSSE).
  • Gonzalez-Sanchez A, Frausto-Solis J, Ojeda-Bustamante W (2014). Predictive ability of machine learning methods for massive crop yield prediction. Spanish Journal of Agricultural Research 12(2): 313-328.
  • Hadjout D, Torres J, Troncoso A, Sebaa A, Martínez-Álvarez F (2022). Electricity consumption forecasting based on ensemble deep learning with application to the Algerian market. Energy 243: 123060.
  • Iniyan S, Varmaa VA, Naidu CT (2023). Crop yield prediction using machine learning techniques. Advances in Engineering Software 175: 103326.
  • Jain N, Kumar A, Garud S, Pradhan V, Kulkarni P (2017). Crop selection method based on various environmental factors using machine learning. International Research Journal of Engineering and Technology 4(2): 56-72.
  • Jiang D, Yang X, Clinton N, Wang N (2004). An artificial neural network model for estimating crop yield using remotely sensed information. Int. J. Remote Sensing 25: 1723-1732.
  • Kalichkin VK, Alsova OK, Maksimovich KY (2021). Application of the decision tree method for predicting the yield of spring wheat. AGRITECH-V-2021 IOP Conf. Series: Earth and Environmental Science 839: 032042 https://doi.org/10.1088/1755-1315/839/3/032042
  • Kaul M, Hill RL, Walthall C (2005). Artificial neural networks for corn and soybean yield prediction. Agric. Syst 85: 1–18.
  • Kayad A, Sozzi M, Gatto S, Marinello F, Pirotti F (2019). Monitoring within-field variability of corn yield using Sentinel-2 and machine learning techniques. Remote Sensing 11(23): 2873.
  • Khaki S, Wang L (2019). Crop yield prediction using deep neural networks. Frontiers in Plant Science 10: 452963.
  • Laxmi RR, Kumar A (2011). Weather based forecasting model for crops yield using neural network approach. Statistics and Applications 9(1), 55-69.
  • Leroux L, Castets M, Baron C, Escorihuela MJ, Bégué A, Seen DL (2019). Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices. European Journal of Agronomy 108: 11-26.
  • Liu J, Goering CE, Tian L (2001). Neural network for setting target corn yields. T ASAE 44(3): 705-713.
  • Mishra S, Mishra D, Santra G (2016). Applications of machine learning techniques in agricultural crop production: A review paper. Indian Journal of Science and Technology 9(38).
  • Mok H-F, Dassanayake KB, Hepworth G, Hamilton AJ (2014). Field comparison and crop production modeling of sweet corn and silage maize (Zea mays L.) with treated urban wastewater and freshwater. Irrigation Science 32(5): 351–368. https://doi.org/10.1007/s00271-014-0434-4.
  • Mourtzinis S, Esker PD, Specht JE, Conley SP (2021). Advancing agricultural research using machine learning algorithms. Scientific Reports 11(1): 1–7.
  • Narin OG, Abdikan S (2022). Monitoring of phenological stage and yield estimation of sunflower plant using Sentinel-2 satellite images, Geocarto International 37(5): 1378-1392, https://doi.org/10.1080/10106049.2020.1765886.
  • Önder M, Öztürk Ö, Ceyhan E (2001). Yağlık ayçiçeği çeşitlerinin verim ve bazı verim unsurlarının belirlenmesi. S.Ü. Ziraat Fakültesi Dergisi 15 (28): 136-146.
  • Palatnik RR, Roson R (2012). Climate change and agriculture in computable general equilibrium models: alternative modeling strategies and data needs. Climatic Change 112: 1085–1100.
  • Paudel D, Boogaard H, de Wit A, Janssen S, Osinga S, Pylianidis C, Athanasiadis I (2021). Machine learning for large-scale crop yield forecasting. Agricultural Systems 187(1).
  • Priyadharshini K, Prabavathi R, Devi VB, Subha P, Saranya SM, Kiruthika K (2022). An enhanced approach for crop yield prediction system using linear support vector machine model. In 2022 IEEE International Conference on Communication, Computing, and Internet of Things (IC3IoT) :1-5.
  • Putt ED (1977). Early history of sunflowers. In: A.A. Schneiter (ed). Sunflower Technology and Production. ASACSSA and SSSA Madison, WI. p. 1-19.
  • Republic of Turkey Ministry of Agriculture and Forestry, Sunflower Bulletin, 20 May, 2022
  • Rokach L, Maimon O (2014). Data mining with decision tree; series in machine perception and artificial intelligence. World Scientific 81: 61-62.
  • Salam A, El Hibaoui A. (2021) Energy consumption prediction model with deep inception residual network inspiration and LSTM. Math. Comput. Simul 190: 97–109.
  • Singh R, Singh G (2017). Wheat crop yield assessment using decision tree algorithms. International Journal of Advanced Research in Computer Science 8(5):1809-1817.
  • Tang XY, Yang WW, Liu Z, Li JC, Ma X (2024). Deep learning performance prediction for solar-thermal-driven hydrogen production membrane reactor via bayesian optimized LSTM. International Journal of Hydrogen Energy 82, 1402-1412.
  • URL1 https://biruni.tuik.gov.tr (access date: 06.06.2023).
  • URL2 https://data.tuik.gov.tr (access date: 04.08.2023).
  • URL3 Turkish State Meteorological Service, https://www.mgm.gov.tr (access date: 23.09.2023).
  • USDA (2020). U.S. Department of Agriculture, Oil crops yearbook, https://www.ers. usda.gov/data-products/oil-crops-yearbook/oil-Crops-Yearbook/ (access date:02.10.2020)
  • Xu L, Hou L, Zhu Z, Li Y, Liu J, Lei T, Wu X (2021). Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm. Energy 222: 119955.
  • Yan, X., Ji, X., Meng, Q., Sun, H., & Lei, Y. (2024). A hybrid prediction model of improved bidirectional long short-term memory network for cooling load based on PCANet and attention mechanism. Energy 292: 130388.
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Ziraat Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Seda Hatice Gökler 0000-0001-8786-1193

Erken Görünüm Tarihi 13 Aralık 2024
Yayımlanma Tarihi 16 Aralık 2024
Gönderilme Tarihi 3 Nisan 2024
Kabul Tarihi 3 Ekim 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 38 Sayı: 3

Kaynak Göster

EndNote Gökler SH (01 Aralık 2024) Sunflower Crop Yield Prediction Using Machine Learning Methods. Selcuk Journal of Agriculture and Food Sciences 38 3 445–462.

Selcuk Journal of Agriculture and Food Sciences Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.