Comparison Between Machine Learning Models for Yield Forecast in Cocoa Crops in Santander, Colombia
DOI:
https://doi.org/10.19053/01211129.v29.n54.2020.10853Keywords:
agricultural-yield, agroforestry-system, cocoa, machine-learning, prediction, productivityAbstract
The identification of influencing factors in crop yield (kg·ha-1) provides essential information for decision-making processes related to the prediction and improvement of productivity, which gives farmers the opportunity to increase their income. The current study investigates the application of multiple machine learning algorithms for cocoa yield prediction and influencing factors identification. The Support Vector Machines (SVM) and Ensemble Learning Models (Random Forests, Gradient Boosting) are compared with Least Absolute Shrinkage and Selection Operator (LASSO) regression models. The considered predictors were climate conditions, cocoa variety, fertilization level and sun exposition in an experimental crop located in Rionegro, Santander. Results showed that Gradient Boosting is the best prediction alternative with Coefficient of determination (R2) = 68%, Mean Absolute Error (MAE) = 13.32, and Root Mean Square Error (RMSE) = 20.41. The crop yield variability is explained mainly by the radiation one month before harvest, the accumulated rainfall on the harvest month, and the temperature one month before harvest. Likewise, the crop yields are evaluated based on the kind of sun exposure, and it was found that radiation one month before harvest is the most influential factor in shade-grown plants. On the other hand, rainfall and soil moisture are determining variables in sun-grown plants, which is associated with the water requirements. These results suggest a differentiated management for crops depending on the kind of sun exposure to avoid compromising productivity, since there is no significant difference in the yield of both agricultural managements.
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D. Jiménez, J. Cock, A. Jarvis, J. Garcia, H. F. Satizábal, P. Van-Damme, A. Peréz-Uribe, and M. Barreto-Sanz, “Interpretation of commercial production information: A case study of lulo (Solanum quitoense), an under-researched Andean fruit,” Agricultural Systems, vol. 104 (3), pp. 258-270, Mar. 2011. https://doi.org/10.1016/j.agsy.2010.10.004
J. W. Jones, J. M. Antle, B. Basso, K. J. Boote, R. T. Conant, I. Foster, H. C. J. Godfay, M. Herrero, R. E. Howitt, S. Janssen, B. A. Keating, R. Munoz-Carpena, C. H. Porter, C. Rosenzweig, and T. R. Wheeler, “Brief history of agricultural systems modeling,” Agricultural Systems, vol. 155, pp. 240-254, Jul. 2017. https://doi.org/10.1016/j.agsy.2016.05.014
I. Diaz, S. M. Mazza, E. F. Combarro, L. I. Gimenez, and J. E. Gaiad, “Machine learning applied to the prediction of citrus production,” Spanish Journal of Agricultural Research, vol. 15 (2), e0205, Jun. 2017. https://doi.org/10.5424/sjar/2017152-9090
S. T. Drummond, K. A. Sudduth, A. Joshi, S. J. Birrell, and N. R. Kitchen, “Statistical and neural methods for site-specific yield prediction,” Transactions of the ASAE, vol. 46 (1), pp. 5-14, 2003. https://doi.org/10.13031/2013.12541
J. L. De Paepe, and R. Alvarez, “Wheat Yield Gap in the Pampas: Modeling the Impact of Environmental Factors,” Agronomy, Soils & Environmental Quality, vol. 108 (4), pp. 1367-1378, 2016. https://doi.org/10.2134/agronj2015.0482
J. D. R. Soares, M. Pasqual, W. S. Lacerda, S. O. Silva, and S. L. R. Donato, “Comparison of techniques used in the prediction of yield in banana plants,” Scientia Horticulturae, vol. 167, pp. 84-90, Mar. 2014. https://doi.org/10.1016/j.scienta.2013.12.012
A. Shekoofa, Y. Emam, N. Shekoufa, M. Ebrahimi, and E. Ebrahimie, “Determining the Most Important Physiological and Agronomic Traits Contributing to Maize Grain Yield through Machine Learning Algorithms: A New Avenue in Intelligent Agriculture,” PLoS One, vol. 9 (5), e97288, May 2014. https://doi.org/10.1371/journal.pone.0097288
J. R. Romero, P. F. Roncallo, P. C. Akkiraju, I. Ponzoni, V. C. Echenique, and J. A. Carballido, “Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires,” Computers and Electronics in Agriculture, vol. 96, pp. 173-179, Aug. 2013. https://doi.org/10.1016/j.compag.2013.05.006
X. Huang, G. Huang, C. Yu, S. Ni, and L. Yu, “A multiple crop model ensemble for improving broad-scale yield prediction using Bayesian model averaging,” Field Crops Research, vol. 211, pp. 114-124, Sep. 2017. https://doi.org/10.1016/j.fcr.2017.06.011
A. A. V. da Silva, I. A. F. Silva, M. C. M. Teixeira Filho, S. Buzetti, and M. C. M. Teixeira, “Estimate of wheat grain yield as function of nitrogen fertilization using neuro fuzzy modeling,” Revista Brasileira de Engenharia Agrícola e Ambiental, vol. 18 (2), pp. 180-187, Feb. 2014. https://doi.org/10.1590/S1415-43662014000200008
I. Lopez, J. Plazas, and J. C. Corrales, “A tool for classification of cacao production in Colombia based on multiple classifier systems,” in 17th International Conference Computational Science and Its Applications – ICCSA 2017, Trieste, Italy, Jul. 2017. https://doi.org/10.1007/978-3-319-62395-5_5
E. Somarriba, and J. Beer, “Productivity of Theobroma cacao agroforestry systems with timber or legume service shade trees,” Agroforestry Systems, vol. 81, pp. 109-121, 2011. https://doi.org/10.1007/s10457-010-9364-1
P. A. Zuidema, P. A. Leffelaar, W. Gerritsma, L. Mommer, and N. P. R. R. Anten, “A physiological production model for cocoa (Theobroma cacao): model presentation, validation and application,” Agricultural Systems, vol. 84 (2), pp. 195-225, May 2005. https://doi.org/10.1016/j.agsy.2004.06.015
L. F. García Carrión, Catalogo de cultivares de cacao del Perú, Lima: Ministerio de Agricultura y Riego, 2010.
V. Vapnik, The nature of Statistical Learning Theory, New York: Springer-Verlag, 1995.
H. Drucker, C. J. C. Burges, L. Kaufman, A. J. Smola, and V. Vapnik, "Support Vector Regression Machines," Neural Information Processing Systems, vol. 9, pp. 1-11, 1997.
T. Dietterich, Ensemble Methods in Machine Learning. In: Multiple Classifier Systems, Heidelberg: Springer Berlin, 2000.
J. H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics, vol. 29 (5), pp. 1189-1232, 2001.
L. Breiman, “Random forests,” Machine Learning, vol. 45 (1), pp. 5-32, 2001. https://doi.org/10.1023/A:1010933404324
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, and B. Thirion, “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
T. M. Logan, S. McLeod, and S. Guikema, “Predictive models in horticulture: A case study with Royal Gala apples,” Scientia Horticulturae, vol. 209, pp. 201-213, Sep. 2016. https://doi.org/10.1016/j.scienta.2016.06.033
A. Daymond, and P. Hadley, “The effects of temperature and light integral on early vegetative growth and chloroplyll fluorescence of four contrasting genotypes of cacao,” Annals of Applied Biology, vol. 145 (3), pp. 257-262, 2004. https://doi.org/10.1111/j.1744-7348.2004.tb00381.x
Y. Ahenkorah, B. Halm, M. Appiah, and G. Akrofi, “Twenty Years’ Results from a Shade and Fertilizer Trial on Amazon Cocoa (Theobroma cacao) in Ghana,” Experimental Agriculture, vol. 23 (1), pp. 31-39, Jan. 1987. https://doi.org/10.1017/s0014479700003380
O. Deheuvels, J. Avelino, E. Somarriba, and E. Malezieux, “Vegetation structure and productivity in cocoa-based agroforestry systems in Talamanca, Costa Rica,” Agriculture, Ecosystems & Environment, vol. 149, pp. 181-188, Mar. 2012. https://doi.org/doi: 10.1016/j.agee.2011.03.003
W. Vanhove, N. Vanhoudt, and P. Van Damme, “Effect of shade tree planting and soil management on rehabilitation success of a 22-year-old degraded cocoa (Theobroma cacao L.) plantation,” Agriculture, Ecosystems & Environment, vol. 219, pp. 14-25, Mar. 2016. https://doi.org/doi: 10.1016/j.agee.2015.12.005
B. Utomo, A. A. Prawoto, S. Bonnet, A. Bangviwat, and S. H. Gheewala, “Environmental performance of cocoa production from monoculture and agroforestry systems in Indonesia,” Journal of Cleaner Production, vol. 134 (Part B), pp. 583-591, Oct. 2016. https://doi.org/10.1016/j.jclepro.2015.08.102
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