Model Selection of Hybrid Feature Fusion for Coffee Leaf Disease Classification

Abstract

Coffee leaf diseases can significantly impact the productivity and quality of the crops. Accurate and timely identification of these diseases is crucial for effective management and control. This paper proposes a hybrid feature fusion approach for identifying coffee leaf disease, including early and late feature fusion. First, we propose several hybrid models to extract the information feature in the input images by combining MobileNetV3, Swin Transformer, and variational autoencoder (VAE). MobileNetV3, acting on the inductive bias of locality, can extract image features that are closer to one another (local features), while the Swin Transformer is able to extract feature interactions that are further apart (high-level features). These differently extracted features contain complementary information that enriches a unified feature map. Second, the extracted images from models are fused in the early fusion network. The early-fusion learner network is deployed to learn the rich information from the extracted feature. The late fusion network is implemented to comprehensively learn the fused feature before a classification network classifies coffee leaf diseases. The proposed hybrid feature fusion approach is evaluated on a challenging, real world Robusta Coffee Leaf (RoCoLe) dataset with various diseases, including red spider mite and leaf rust disease. The results show that our approach, the hybrid feature fusion of MobileNetV3 and Swin Transformer, outperforms the individual models with an accuracy of 84.29%. In conclusion, the hybrid feature fusion approach combining MobileNetV3 and Swin Transformer models is a promising approach for coffee leaf disease identification, providing accurate and timely diagnosis for effective management and control of the diseases in real-world conditions.