Optimization of Mango Plant Leaf Disease Classification Using Concatenation Method of MobileNetV2 and DenseNet201 CNN Architectures

Abstract

Purpose: Mango production can be severely impacted by diseases affecting mango plants. By leveraging artificial intelligence, the agricultural sector can automate the analysis of mango leaves to monitor plant health. The goal of this research is to improve the early detection of diseases in mango leaves to allow early treatment to minimize damage to the crops.

Methods: This study employs an approach of combining two pre-trained CNN architectures, namely MobileNetV2 and DenseNet201 through concatenation method. To enhance the model’s generalization ability, various image augmentation techniques were applied during the training phase.

Result: The model developed in this study achieved great performance in classifying mango leaf diseases with a testing accuracy of 99.25%. This result indicates the effectiveness of the concatenation method by outperforming the accuracy of either MobileNetV2 or DenseNet201 when implemented separately.

Novelty: This research introduces a novel strategy by concatenating two pre-trained CNN architectures for mango leaf disease classification, a method not previously explored in this context. The model developed from this study has the potential to serve as a tool for the early detection and treatment of mango leaf diseases.