Abstract
Agriculture has a substantial impact on the global economy. One of the main threats to agriculture in the long term is climate change and other environmental factors. The primary determinant of the crop yield is weather conditions (e.g., rain, temperature, etc.). Historical data, e.g., weather, soil, and historic crop yield, are all taken into account for yield predictions. Previously developed models had lower accuracy, even though they had used many datasets that cluttered the models. This paper introduces machine learning methods to predict the food crop yield capacity of 10 food crops, that are widely consumed around the world. Several machine learning algorithms, including Random Forest Regressor, Linear Regression and decision tree Classifier are implemented on various data sets to perform a crop yield prediction. The random forest regression model is used to achieve higher accuracy using different datasets like temperature, rainfall, yield etc. It has been found that the accuracy for Multiple Linear Regression is 11.9%, the accuracy for Random Forest Regression is 99.8% and the accuracy for decision tree classifier is 97.8%.
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References
Lekhaa, T.: Efficient crop yield and pesticide prediction for improving agricultural economy using data mining techniques. Int. J. Mod. Trends Eng. Sci. (IJMTES) 3(10), 11–28 (2016)
Tidake, A.H.: Design and implement a novel algorithm to maximize the yield of farming using prescriptive analysis (2019)
Khosla, E., Dharavath, R., Priya, R.: Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression. Environ. Dev. Sustain. 22(6), 5687–5708 (2019). https://doi.org/10.1007/s10668-019-00445-x
McBratney, A., Whelan, B., Ancev, T., Bouma, J.: Future directions of precision agriculture. Precision Agric. 6, 7–23 (2005). https://doi.org/10.1007/s11119-005-0681-8
Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R.L., Mouazen, A.M.: Wheat yield prediction using machine learning and advanced sensing techniques. Comput. Electron. Agric. 121, 57–65 (2016)
Chlingaryan, A., Sukkarieh, S., Whelan, B.: Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: a review. Comput. Electron. Agric. 151, 61–69 (2018)
Veenadhari, S., Misra, B., Singh, C.: Machine learning approach for forecasting crop yield based on climatic parameters. In: 2014 International Conference on Computer Communication and Informatics, pp. 1–5. IEEE (2014)
Palanivel, K., Surianarayanan, C.: An approach for prediction of crop yield using machine learning and big data techniques. Int. J. Comput. Eng. Technol. 10, 110–118 (2019)
Pa, R., Gb, B.J.: A review based on secure banking application against server attacks. In: Smart Intelligent Computing and Communication Technology, vol. 38, p. 241 (2021)
Marquez, F.P.G., Tercero, D.J.P., Schmid, F.: Unobserved component models applied to the assessment of wear in railway points: a case study. Eur. J. Oper. Res. 176, 1703–1712 (2007)
Acaroğlu, H., García Márquez, F.P.: Comprehensive review on electricity market price and load forecasting based on wind energy. Energies 14, 7473 (2021)
García Márquez, F.P., Peinado Gonzalo, A.: A comprehensive review of artificial intelligence and wind energy. Arch. Comput. Methods Eng. 29, 2935–2958 (2022). https://doi.org/10.1007/s11831-021-09678-4
Jiménez, A.A., Zhang, L., Muñoz, C.Q.G., Márquez, F.P.G.: Maintenance management based on machine learning and nonlinear features in wind turbines. Renew. Energy 146, 316–328 (2020)
Peco Chacón, A.M., Segovia Ramírez, I., García Márquez, F.P.: State of the art of artificial intelligence applied for false alarms in wind turbines. Arch. Comput. Methods Eng. 29, 2659–2683 (2022). https://doi.org/10.1007/s11831-021-09671-x
García Márquez, F.P., García-Pardo, I.P.: Principal component analysis applied to filtered signals for maintenance management. Qual. Reliab. Eng. Int. 26, 523–527 (2010)
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Ramisetty, U.M., Gundavarapu, V.N.K., Rajender, R., Segovia Ramírez, I., García Márquez, F.P. (2023). Prediction Analysis of Crop and Their Futuristic Yields Using Random Forest Regression. In: García Márquez, F.P., Segovia Ramírez, I., Bernalte Sánchez, P.J., Muñoz del Río, A. (eds) IoT and Data Science in Engineering Management. CIO 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 160. Springer, Cham. https://doi.org/10.1007/978-3-031-27915-7_50
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