HibridNet: Rede Neural Convolucional (CNN) Híbrida para classificação de doenças em folhas de bananeira

Abstract

Banana cultivation faces significant challenges due to foliar diseases such as black sigatoka, yellow sigatoka, Panama disease, and cordona, which reduce productivity and increase production costs. Traditional disease detection methods are often limited in accuracy and scalability, highlighting the need for automated solutions. This study proposes the implementation and evaluation of convolutional neural networks (CNNs) based on LeNet and Vision Transformer (ViT) architectures. Additionally, a novel hybrid model, named HibridNet, is introduced by combining the strengths of both architectures. Experimental results show that HibridNet achieves higher accuracy compared to individual ViT and LeNet models. The proposed hybrid approach demonstrates significant potential to support disease management in banana cultivation, improving productivity and reducing operational costs