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Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data

Author

Listed:
  • Juan Rincon-Patino

    (Grupo de ingeniería Telemática, Universidad del Cauca, Campus Tulcán, Popayán 190002, Colombia)

  • Emmanuel Lasso

    (Grupo de ingeniería Telemática, Universidad del Cauca, Campus Tulcán, Popayán 190002, Colombia)

  • Juan Carlos Corrales

    (Grupo de ingeniería Telemática, Universidad del Cauca, Campus Tulcán, Popayán 190002, Colombia)

Abstract

Persea americana , commonly known as avocado, is becoming increasingly important in global agriculture. There are dozens of avocado varieties, but more than 85% of the avocados harvested and sold in the world are of the Hass one. Furthermore, information on the market of agricultural products is valuable for decision-making; this has made researchers try to determine the behavior of the avocado market, based on data that might affect it one way or another. In this paper, a machine learning approach for estimating the number of units sold monthly and the total sales of Hass avocados in several cities in the United States, using weather data and historical sales records, is presented. For that purpose, four algorithms were evaluated: Linear Regression, Multilayer Perceptron, Support Vector Machine for Regression and Multivariate Regression Prediction Model. The last two showed the best accuracy, with a correlation coefficient of 0.995 and 0.996, and a Relative Absolute Error of 7.971 and 7.812, respectively. Using the Multivariate Regression Prediction Model, an application that allows avocado producers and sellers to plan sales through the estimation of the profits in dollars and the number of avocados that could be sold in the United States was created.

Suggested Citation

  • Juan Rincon-Patino & Emmanuel Lasso & Juan Carlos Corrales, 2018. "Estimating Avocado Sales Using Machine Learning Algorithms and Weather Data," Sustainability, MDPI, vol. 10(10), pages 1-12, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3498-:d:172844
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    References listed on IDEAS

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