Leaf disease classification with Multiple-model deep learning

Abstract

The wilting of leaves caused by disease poses risks to both harvest yield and the environment. Therefore, the timely detection of disease signs on leaves is crucial to enable farmers to prevent disease outbreaks and safeguard their crops. However, manually observing all diseased leaves on a large scale demands substantial time and human effort. In this study, we propose an effective method for automated disease detection on leaves. Specifically, this method utilizes images captured from mobile phones. The proposed technique combines four models (ensemble of models) with distinct features: (1) ResNeXt50 model with a high-quality image processing, (2) ViT model with a low-quality image processing, (3) Efficientnet B5 model combines a self-learning with noisy input, and (4) Mobilenet V3 model with image segmentation. Experimental results demonstrate that the proposed method outperforms some of the state-of-the-art methods on TLU-Leaf dataset (ours) with F1-score of 90% and Cassava Leaf Disease dataset with F1-score of 87%.