Roses Greenhouse Cultivation Classification Using Machine Learning Techniques

Abstract

There is a demand for flowers globally all year round, more particularly roses, necessitating increased production for flowers. Demand for roses has increased due to their year-long availability as well as its uses in cosmetic, perfume, medicinal products, food raw materials and decoration industry. Rose plants are prone to drastic fluctuations in temperature, drought stress damages, and low precipitations. These resulted to an increase in greenhouse production to generate optimum supply to meet growing demands as controlled environment provides several advantages. In this study, four machine learning models (random forest, support vector machine, multinomial logistic regression, and artificial neural networks) were applied to roses greenhouse cultivation dataset. The study aims to classify the most suitable greenhouse environment to upgrade the roses state leading to the optimal production of roses. Four model configurations corresponding to the pre-processing techniques were tested. These were scaling only, scaling plus removal of outliers, scaling plus SMOTE, and scaling with removal of outliers plus SMOTE. Random forest with all pre-processing steps applied to the dataset obtained the best performance with the highest weighted F1- scores, weighted-average precision, weighted-average recall, and Cohen's kappa statistic. This indicates that machine learning models can predict corrective actions leading to improved conditions of roses. The notable contribution of this research is to find valid and reliable classification models that assist growers in predicting the best greenhouse micro-environment.