Abstract
In this paper a rice crop prediction performance analysis of five machine learning and two multilinear regression algorithms is presented. A five hectares rice plot was selected. For the database, in the plot, 72 sampling points spatially distributed were defined. For each sampling point, physicochemical, biomass and leaf chlorophyll content measurement were taken at vegetative stage. Additionally, the plot was flown with a quadcopter to take multispectral images in order to calculate vegetation indices maps. As output variable, the crop yield was defined. The machine learning (ML) algorithms used in this analysis were: Random Forest, eXtreme Gradient Boosting, Support Vector Regression Machines, Multilayer Perceptron Regression Neural Networks, and K-Nearest Neighbors; the multilinear algorithms were Partial Least Squares and Multiple Linear regression (MLR). The results show the best performance for K-Nearest Neighbors with an average absolute error for the testing point of 10.74%. The worst case was the MLR with a root mean square error (RMSE) of 2712.26 kg-ha\(^{-1}\) in the testing dataset, while KNN regression was the best with 1029.69 kg-ha\(^{-1}\).
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References
Taylor, P., Altman, N.S., Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression.13050, 37–41 (2014). https://doi.org/10.1080/00031305.1992.10475879
Aslam, M., Mahmood, I.H., Qureshi, R.H., Nawaz, S., Akhtar, J. : Salinity tolerance of rice as affected by boron nutrition. Pak. J. Soil Sci. (Pak.) (2002)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Chen, Y., Lin, Z., Zhao, X., Wang, G., Gu, Y.: Deep learning-based classification of hyperspectral data. IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 7(6), 2094–2107 (2014)
Chen, T., He, T.: Higgs boson discovery with boosted trees. In: HEPML 2014 Proceedings of the 2014 International Conference on High-Energy Physics and Machine Learning, vol. 42, pp. 69–80 (2014)
Dahnke, W.C., Swenson, L.J., Goos, R.J., Leholm, A.G.: Choosing a crop yield goal. SF-822. Fargo: North Dakota State Extension Service (1988)
Das, B., Nair, B., Reddy, V.K., Venkatesh, P.: Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India. Int. J. Biometeorol. 62(10), 1809–1822 (2018). https://doi.org/10.1007/s00484-018-1583-6
Dou, F., Soriano, J., Tabien, R.E., Chen, K.: Soil texture and cultivar effects on rice (Oryza sativa, L.) grain yield, yield components and water productivity in three water regimes. PloS One 11(3), e0150549 (2019)
Filippi, P., Edward, J.J., Niranjan, S.W., Pallegedara, D.S.N.S., Liana, E.P., Sabastine, U.U., Thomas, G.J., Stacey, E.P., Brett, M.W., Thomas, F.A.B.: An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precis. Agric. (2019). https://doi.org/10.1007/s11119-018-09628-4
Forkuor, G., Hounkpatin, O.K., Welp, G., Thiel, M.: High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models. PloS One 12(1), e0170478 (2017)
Garg, A., Garg, B.: A robust and novel regression based fuzzy time series algorithm for prediction of rice yield. In: ICCT 2017 - International Conference on Intelligent Communication and Computational Techniques, pp. 48–54, January 2018. https://doi.org/10.1109/INTELCCT.2017.8324019
González Sánchez, A., Frausto Solís, J., Ojeda Bustamante, W.: Predictive ability of machine learning methods for massive crop yield prediction (2014)
Khanal, S., Fulton, J., Klopfenstein, A., Douridas, N., Shearer, S.: Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield. Comput. Electron. Agric. 153, 213–225 (2018)
Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Team, R.C.: caret: Classification and Regression Training. R package version v6. 0.77 (2017)
Jeong, J.H., Resop, J.P., Mueller, N.D., Fleisher, D.H., Yun, K., Butler, E.E., Kim, S.H.: Random forests for global and regional crop yield predictions. PLoS One 11(6), e0156571 (2016)
Kantanantha, N., Serban, N., Griffin, P.: Yield and price forecasting for stochastic crop decision planning. J. Agric. Biol. Environ. Stat. 15, 362–380 (2010)
Murtagh, F.: Multilayer perceptrons for classification and regression. Neurocomputing 2(5–6), 183–197 (1991). https://doi.org/10.1016/0925-2312(91)90023-5
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). https://doi.org/10.1016/j.compag.2015.11.018
Rani, P.S., Latha, A.: Effect of calcium, magnesium and boron on nutrient uptake and yield of rice in Kole lands of Kerala. Indian J. Agric. Res. 51(4), 388–391 (2017)
Raun, W.R., Solie, J.B., Johnson, G.V., Stone, M.L., Lukina, E.V., Thomason, W.E., et al.: Inseason prediction of potential grain yield in winter wheat using canopy reflectance. Agron. J. 93, 583–589 (2001)
Reza, M.N., Na, I.S., Baek, S.W., Lee, K.H.: Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images. Biosyst. Eng. 177, 109–121 (2019)
The R Development Core Team: A language and environment for statistical 1031 computing. Vienna, Austria: R Foundation for Statistical Computing, vol. 1032 (2019)
Singh, V., Sarwar, A., Sharma, V.: Analysis of soil and prediction of crop yield (Rice) using machine learning approach. Int. J. Adv. Res. Comput. Sci. 8(5), 1254–1259 (2017)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Statistics and Computing, pp. 199-222. Kluwer Academics (2004). https://doi.org/10.1023/B:STCO.0000035301.49549.88
Stafford, J.V.: Implementing precision agriculture in the 21st century. J. Agric. Eng. Res. 76(3), 267–275 (2000). https://doi.org/10.1006/jaer.2000.0577
Zhang, N., Wang, M., Wang, N.: Precision agriculture - a worldwide overview. Comput. Electron. Agric. 36, 113–132 (2002). https://doi.org/10.1016/S0168-1699(02)00096-0
Zhang, L., Zhang, J., Kyei-boahen, S., Zhang, M.: Simulation and prediction of soybean growth and development under field conditions. Am.-Eurasian J. Agric. Environ. Sci 7(4), 374–385 (2010)
Acknowledgement
Authors wish to thank Ministerio de Agricultura y Desarrollo Rural (MADR) for financing this study. This work was supported by project “Manejo por sitio específico del agua de riego, el nitrógeno y las malezas en el sistema de producción Arroz, Maíz-Algodón en el Departamento del Tolima” in agreement between Universidad de Ibagué and Corporación Colombiana de Investigación Agropecuaria-Agrosavia, Centro de Investigación Nataima.
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Barrero, O. et al. (2020). Rice Yield Prediction Using On-Farm Data Sets and Machine Learning. In: El Moussati, A., Kpalma, K., Ghaouth Belkasmi, M., Saber, M., Guégan, S. (eds) Advances in Smart Technologies Applications and Case Studies. SmartICT 2019. Lecture Notes in Electrical Engineering, vol 684. Springer, Cham. https://doi.org/10.1007/978-3-030-53187-4_46
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