Application of Machine Learning in precision agriculture
DOI:
https://doi.org/10.33131/24222208.356Keywords:
Machine Learning, Smart Agriculture, KNN, Accuracy Score, Decision TreeAbstract
This article proposes a Machine Learning model to predict the state of the harvest from information on the consumption of pesticides and other crop variables. A machine learning methodology is followed, which consists of four steps. At first, a stage of preprocessing and analysis of information, and separation of training, test, and validation data. The final stages include the selection of models and evaluation of hyperparameters of the model from a metric. For this, five classification models are proposed, and the accuracy score is taken as a metric for evaluation. As a result, the hyperparameters for every model are obtained, and the best-performing model is selected.
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