Assessment of the relevance of features associated with corn crop yield prediction in Colombia, a country in the Neotropical zone

Abstract

Obtaining highly accurate predictive models to precisely estimate corn crop yields is essential for making informed decisions in a sustainable agriculture environment. There are various approaches to achieve this goal, including models based on Fuzzy Logic, Association Rules, and Machine Learning. However, some of these models have limitations in terms of the accuracy of their predictions, attributable to the high complexity and non-linearity in the interactions between factors. While Machine Learning techniques alone can achieve high precision, the inclusion of multiple attributes can reduce it. This study focuses on identifying the most influential factors at the regional level through a comprehensive analysis of the relevance of features associated with corn crop yields in Colombia, a country in the Neotropical zone. To accomplish this, climatological time series and historical yield records are used through a methodology based on CRISP- DM, widely used in the field of data mining, involving a review of related work, data cleaning and transformation, relevance evaluation using the RReliefF algorithm, and verification of the performance of the most influential factors through prediction algorithms. The results obtained demonstrate that solar radiation, precipitation, vapor pressure, and maximum and minimum temperatures exert the greatest influence on corn crop yield prediction, with a relevance factor of 0.033, 0.032, 0.026, 0.022, and 0.021, respectively. In the validation of the performance of the selected factors, two predictive models were implemented. The first, based on Artificial Neural Networks, yielded a RMSE of 0.1216 with the subset of variables and 0.1403 with all available variables. In the second, Linear Regression was applied, resulting in an RMSE of 0.1417 with the subset of variables and 0.1424 with all available variables. These results highlight the importance of the selected features as the most influential climatic factors in defining highly accurate predictive models in the Neotropical zone.