A bilinear transformer interactive neural networks-based approach to fine-grained recognition and protection of plant diseases for gardening design

Abstract

Landscape gardening design is an important part of urban ecology and urban image, which is of great significance for urban plant protection. Plant diseases are a major challenge that must be faced in the conservation of landscape plants. Having timely and comprehensive information about the types and developmental stages of plant diseases is crucial for effective disease control. Traditional plant disease differentiation, which relies on manual inspection by professionals, is both time-consuming and labor-intensive. The rapid development of deep artificial neural network technology, especially the rise of computer vision technology, provides efficient and accurate technical means for plant disease identification. In this paper, we introduce the Interactive Bilinear Transformer Network (IBTN) model, which utilizes fine-grained recognition technology for landscape plant disease identification. We validate the effectiveness of model using several public datasets, demonstrating its competitive performance.