Blueberry Leaf Disease Classification Using Vision Transformer Technique and Weighted Feature Fusion

Many plant diseases manifest visible symptoms, typically diagnosed by experienced plant pathologists who visually inspect infected plant leaves. However, this manual diagnostic process is slow and heavily dependent on the pathologist's expertise, highlighting its suitability for computer-aided diagnostic systems. Unlike traditional machine learning approaches that demand meticulous manual feature extraction, there is a demand for models capable of successful classification without extensive preprocessing. In this research, we introduced an efficient module that leverages the advantages of vision transformer and weighted feature fusion across raw images, enhanced images, and edge information. Experimental results demonstrated that our proposed system outperformed existing methods such as EfficientNet or DenseNet. Specifically, our proposed system achieved a detection rate and false alarm rate of 98.18% and 0.88%, respectively. In contrast, the EfficientNet attained a detection rate and false alarm rate of 94.24% and 1.56%, respectively, while the DenseNet reached 95.13% and 1.25% for detection rate and false alarm rate, respectively.