Precipitation forecast with logistics regression methods for harvest optimization



Precipitation forecast, Machine learning, Logistic regression, Harvest efficiency, Optimization


This paper proposes a model that forecasts the weather and then, based on that forecast, uses an income-oriented linear programming method to optimize the harvesting process. Data representing a total yearly output capacity of 472,878 tons from 214 different field locations were used to test the model for sugar beet production. Prior to optimization, long-term one-year weather rainfall forecasting was done using 10 years of actual weather data for the field locations. Weather precipitation was forecasted using logistic regression with an accuracy of 84.16%. The outcome of the weather precipitation prediction model was a parameter in the optimization model. The weather forecast for precipitation led to the 120-day harvest planning being optimized. Comparative analysis was done on the outcomes of the developed model and the current scenario. Comparing the current situation to the proposed one, revenue would have increased by 16.7%. Given that it incorporates weather forecasts into the harvest optimization process, the methodology presented in this paper is more practical than other harvest optimization models.


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How to Cite

SAMASTI, M., & KÜÇÜKDENİZ, T. (2023). Precipitation forecast with logistics regression methods for harvest optimization. International Journal of Agriculture, Environment and Food Sciences, 7(1).



Research Article