Cucumber Disease Classification with Ensemble Learning Method for Complex Datasets

Abstract

Many researchers are taking into account the algorithm's ability to detect diseases in plants since it can save expenses and deliver more accurate results. However, there are various obstacles in detecting diseases, particularly in cucumber plants, such as disease similarities and the ability of models to adapt to the information they have. To address this issue, we propose an ensemble learning strategy based on the averaging method to improve the model's ability to generalize to different cucumber plant environments. According to the results, the ensemble learning approach outperforms the feature fusion method with a test accuracy of 94.20% and a loss of 0.01105. Feature fusion and ensemble learning techniques, in general, have the potential to increase the model's capacity to classify difficult data.