Abstract
In order to make strategic decisions about import-export policies and triple farmers’ income, early and precise crop output evaluation is crucial in the agriculture sector. Crop yield predictions are made using machine learning (ML) algorithms, however the agriculture industry faces difficulties with them. Using three tree-based classifiers—Decision tree (DT), Extra tree (ET), and Categorical Boosting (CatBoost)—this study investigated the application of machine learning approaches to forecast agricultural yields. In order to assess the model’s performance, assessment measures such R-squared, Mean Squared Error (MSE), Mean Absolute Error (MAE), and Accuracy were used. A thorough depiction of pertinent agricultural parameters, including weather patterns, soil properties, and past crop yields, may be found within the Kaggle database. According to the results, the ET classifier performed better than the DT and CatBoost classifiers, with the greatest accuracy of 99.15%. These results highlight the effectiveness concerning machine learning techniques in precisely predicting the output of crops, providing insightful information for agricultural decision-making and resource optimization.
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The dataset produced and scrutinized in this study are accessible from the corresponding author upon reasonable request.
References
Madhusudhan L. Agriculture role on Indian economy. Bus Econ J. 2015;6:1.
Kumar V, Dave V, Bhadauriya R, Chaudhary S. Krishimantra: Agricultural recommendation system 1–2.
Food. and O. Agriculture. Key Facts on Food Loss and Waste You Should Know! (2019).
Srinivasan A. Handbook of Precision Agriculture: principles and applications. CRC; 2006.
Gümüşçü A, Tenekeci ME, &Bilgili AV. Estimation of wheat planting date using machine learning algorithms based on available climate data. Sustain Comput Inf Syst. 2020;28:100308.
Navarro-Hellín H, et al. A decision support system for managing irrigation in agriculture. Comput Electron Agric. 2016;124:121–31.
Patil SS, &Thorat SA. Early detection of grapes diseases using machine learning and IoT 1–5. (IEEE).
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. 2018;151:61–9.
Dimitriadis S, &Goumopoulos C. Applying machine learning to extract new knowledge in precision agriculture applications 100–104. (IEEE).
Kang Y, Khan S, Ma X. Climate change impacts on crop yield, crop water productivity and food security–A review. Prog Nat Sci. 2009;19:1665–74.
Chauhan D, Thakur J. Data mining techniques for weather prediction: a review. Int J Recent Innov Trends Comput Commun. 2014;2:2184–9.
Paras SM, Kumar A, Chandra M. A feature based neural network model for weather forecasting. Int J Comput Intell. 2009;4:209–16.
Greig L. An analysis of the key factors influencing farmer’s choice of crop, Kibamba Ward. Tanzan J Agric Econ. 2009;60:699–715.
Apipattanavis S, Bert F, Podestá G, &Rajagopalan B. Linking weather generators and crop models for assessment of climate forecast outcomes. Agric Meteorol. 2010;150:166–74.
Cantelaube P, &Terres J-M. Seasonal weather forecasts for crop yield modelling in Europe. Tellus Dyn MeteorolOceanogr. 2005;57:476–87.
Khosla E, Dharavath R, &Priya R. Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression. Environ Dev Sustain. 2020;22:5687–708.
Kumar R, Singh MP, Kumar P. and J.P. Singh. Crop selection method to maximize crop yield rate using machine learning technique. IEEE 138–45.
Tseng F-H, Cho H-H, Wu H-T. Applying big data for intelligent agriculture-based crop selection analysis. IEEE Access. 2019;7:116965–74.
Pudumalar S et al. Crop recommendation system for precision agriculture 32–6. IEEE.
Priya R, Ramesh D, &Khosla E. Crop prediction on the region belts of India: A Naïve Bayes MapReduce precision agricultural model 99–104. (IEEE).
Malik P, Sengupta S, &Jadon JS. Comparative analysis of soil properties to predict fertility and crop yield using machine learning algorithms 1004–1007. (IEEE).
Paudel D, et al. Machine learning for regional crop yield forecasting in Europe. Field Crops Res. 2022;276:108377.
Phaladisailoed T, Numnonda T. 2018, July. Machine learning models comparison for bitcoin price prediction. In 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 506–511). IEEE.
Prokhorenkova L, Gusev G, Vorobev A, Dorogush AV, Gulin A. CatBoost: unbiased boosting with categorical features. ArXiv. 2019. arXiv: 1706.09516.
Ruder S. An overview of gradient descent optimization algorithms. ArXiv. 2016. arXiv: 1609.04747.
Quinlan JR. Induction of decision trees. Mach Learn. 1986;1:81–106.
Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach Learn. 2006;63:3–42.
Elavarasan D, Vincent DR, Srinivasan PMK, Chang C-Y. A hybrid CFS filter and RF-RFE wrapper-based feature extraction for enhanced agricultural crop yield prediction modeling, Agriculture, vol. 10, no. 9, p. 400, Sep. 2020.
Ali M, Deo RC, Downs NJ, Maraseni T. Multi-stage committee based extreme learning machine model incorporating the influence of climate parameters and seasonality on drought forecasting, Comput. Electron. Agricult., vol. 152, pp. 149–165, Sep. 2018.
Deepa N, Ganesan K. Hybrid rough fuzzy soft classifier based multi-class classification model for agriculture crop selection, Soft Comput., vol. 23, no. 21, pp. 10793–10809, Nov. 2019.
Rousson V, Goşoniu NF. An R-square coefficient based on final prediction error, Stat. Methodol., vol. 4, no. 3, pp. 331–340, Jul. 2007.
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The authors acknowledged the VISTAS, Chennai, Tamil Nadu, India for supporting the research work by providing the facilities.
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Kuppan, P., Priya, V.V. Crop Yield Prediction Using Ensemble Machine Learning Techniques. SN COMPUT. SCI. 5, 1160 (2024). https://doi.org/10.1007/s42979-024-03536-3
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DOI: https://doi.org/10.1007/s42979-024-03536-3