Journal of ISMAC
Abstract
Crops selection process is one of the crucial factors involved in increasing agricultural production while reducing wastage. Conventional techniques that are based on farmer’s experiences tend to produce inconsistent results since the environment varies from place to place. In this research, an ensemble approach based on the stacking technique is suggested. The hybrid approach incorporates Adaptive Boosting (AdaBoost) and Light Gradient Boosting Machine (LightGBM) as base models and Logistic Regression as the meta-model. In particular, the hybrid approach makes use of seven input variables which include nutrient contents in soils (nitrogen, phosphorus, and potassium), pH level, temperature, humidity, and rainfall. Various evaluation metrics such as accuracy, precision, recall, and F1-scores are used to evaluate the suggested model. Results indicate that the suggested hybrid model produces an accuracy rate of 99.12%, which beats individual model's performance. The superior performance of the model is due to adaptive boosting and gradient boosting algorithms. Moreover, the model is implemented using a graphical user interface, which is used for predicting crops in real-time
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