Predicción de heladas y variables meteorológicas relevantes en agricultura en la sabana de Bogotá usando Machine Learning

Robinson Castillo Méndez; Julian Andrés Camacho Castro

doi:10.14482/inde.43.01.155.454

Authors

Robinson Castillo Méndez Servicio Nacional de Aprendizaje – SENA (Colombia) https://orcid.org/0000-0002-7412-6306
Julian Andrés Camacho Castro Servicio Nacional de Aprendizaje – SENA (Colombia) https://orcid.org/0009-0004-3545-5539

DOI:

https://doi.org/10.14482/inde.43.01.155.454

Keywords:

Machine Learning, Frost forecasting, Feedforward neural network, Multiple linear regression, Meteorological conditions

Abstract

Taking into account historical information on climatological and frost variables, it is possible to improve decisions made in agricultural activities, seeking to determine patterns that guarantee greater yield and quality of crops and implementing forecast models based on machine learning (ML). This work presents the development of a ML model that allows determining the behavior of the meteorological variables, temperature, rainfall and relative humidity, as well as frost in the Sabana de Bogotá. The starting point was the creation of a historical database of these variables from 2010 to April 2023, considering information from ten different meteorological stations in the region. It has been necessary to implement data imputation techniques in information gaps. To determine the model with the response closest to reality, a model based on multiple linear regression and another on artificial neural networks were developed. According to the results obtained and the level of absolute error, the second model approximates its forecasts closer to the real data. The work developed can be an essential tool to generate an early warning system that helps farmers in the Sabana de Bogotá.

References

M. K. Sott et al., “Precision Techniques and Agriculture 4.0 Technologies to Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends,” IEEE Access, vol. 8, pp. 149854–149867, 2020, doi: 10.1109/ACCESS.2020.3016325.

J. P. Tovar-Soto, M. O. Gonzalez, and J. A. S. Sanchez, “Digital agriculture for urban crops: design of an IoT platform for monitoring variables.,” in 2022 IEEE International Conference on Automation/25th Congress of the Chilean Association of Automatic Control: For the Development of Sustainable Agricultural Systems, ICA-ACCA 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/ICA-ACCA56767.2022.10006036.

M. Barooni, K. Ziarati, and A. Barooni, “Frost Prediction Using Machine Learning Methods in Fars Province,” in 2023 28th International Computer Conference, Computer Society of Iran, CSICC 2023, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/CSICC58665.2023.10105391.

C. A. Ramírez Gómez, “Aplicación del Machine Learning en agricultura de precisión” Revista Cintex, vol. 25, no. 2, pp. 14–27, 2020.

R. L. F. Cunha, B. Silva, and M. A. S. Netto, “A scalable machine learning system for pre-season agriculture yield forecast,” Proceedings - IEEE 14th International Conference on eScience, e-Science 2018, pp. 423–430, 2018, doi: 10.1109/eScience.2018.00131.

A. Sharma, A. Jain, P. Gupta, and V. Chowdary, “Machine Learning applications for precision agriculture: A comprehensive review,” IEEE Access, vol. 9, pp. 4843–4873, 2021, doi: 10.1109/ACCESS.2020.3048415.

S. Elghamrawy, “An AI-Based Prediction Model for Climate Change Effects on Crop production using IoT,” in 2023 International Telecommunications Conference, ITC-Egypt 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 497–503. doi: 10.1109/ITC-Egypt58155.2023.10206201.

H. S. García Cañón, “Implementación de técnicas de machine learning para la predicción de variables meteorológicas y del suelo que afectan la agricultura,” Universidad de los Andes, 2019.

D. F. Gonzalez Botero, “Planteamiento de un modelo de predicción de heladas en cultivos de rosa en la sabana de Bogotá,” Universidad Militar Nueva Granada, Bogotá D.C., 2018.

M. Á. Marqués Gozalbo, “Modelos predictivos de producción agroindustrial con Machine Learning a partir de fuentes de información pública,” Universidad de Córdoba, 2020.

M. G. Herabad and N. P. Afshar, “Fuzzy-based Deep Reinforcement Learning for Frost Forecasting in IoT Edge-enabled Agriculture,” in Proceedings - 2022 8th International Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/ICSPIS56952.2022.10044063.

W. Lv, H. Huang, W. Tang, and T. Chen, “Research and Application of Intersection Similarity Algorithm Based on KNN Classification Model,” in Proceedings - 2021 International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2021, Institute of Electrical and Electronics Engineers Inc., May 2021, pp. 141–144. doi: 10.1109/CAIBDA53561.2021.00037.

M. Akhiladevi, K. Anitha, K. Amrutha, M. Amrutha, and K. R. Chandanashree, “Accident Prediction Using KNN Algorithm,” in 4th International Conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/ICERECT56837.2022.10059746.

M. R. Putri, I. G. P. S. Wijaya, F. P. A. Praja, A. Hadi, and F. Hamami, “The Comparison Study of Regression Models (Multiple Linear Regression, Ridge, Lasso, Random Forest, and Polynomial Regression) for House Price Prediction in West Nusa Tenggara,” in ICADEIS 2023 - International Conference on Advancement in Data Science, E-Learning and Information Systems: Data, Intelligent Systems, and the Applications for Human Life, Proceeding, Institute of Electrical and Electronics Engineers Inc., 2023. doi: 10.1109/ICADEIS58666.2023.10270916.

MathWorks®, “Bayesian regularization backpropagation,” Bayesian regularization backpropagation. Accessed: Nov. 25, 2023. [Online]. Available: https://la.mathworks.com/help/deeplearning/ref/trainbr.html?lang=en