Machine Learning-Based Prediction of Paprika Fruit Production in Korea

Korea produces comparatively less summer-cultivated paprika from August to September due to unfavorable environmental circumstances, particularly high temperatures that begin in late June. As a result of this, managing a greenhouse climate well during the hot season is essential to boosting paprika production and ensuring its sustainability in relation to agricultural expansion. This study aims to analyze the efficient use of environmental characters and to minimize prediction errors with the help of data collected from an advanced environmental controlled system installed in a greenhouse, along with frequently used machine learning techniques, as a sustainable method to adopt. Artificial intelligence (AI)-based sustainable methods were used for evaluating the cumulative paprika fruit count (Pn.f) based on the greenhouse environmental data using multiple linear regression (MLR), support vector machine (SVM), ensemble tree bagging (ET Bagged), and Random Forest (RF). The twenty-one potential indicators/input parameters, comprised of relative humidity, humidity deficit and temperature-related variables, were utilized. A number of statistical tests, including R-square (R2), root mean square error (RMSE), and mean absolute error (MAE), were employed to further bolster the performance efficiency of the computed prediction models. To determine the precise impact of the input parameters, parametric and sensitivity tests were also carried out. The findings show that the suggested models meet all the accuracy-related acceptance criteria. SVM and RF, however, perform better than MLR and ET Bagged. The models’ robustness and dependability are asserted not only by simple correlation but also by the physical and logical relationships between the sensitive elements and the dependent variable. The significance of our work lies in the use of SVM and RF models for the efficient handling of environmental factors, and the findings also advise greenhouse growers on how to modify these climatic variables for greenhouses.