IBAC-Net: integrative brightness adaptive plant leaf disease classification

As agricultural technology continues to advance, effective classification of agricultural diseases are crucial for improving crop yield and quality. This study aims to explore an innovative approach to agricultural disease image classification based on a novel image classification model architecture. First, we design a novel model architecture for image classification that better integrates shallow and deep features. Secondly, to address potential brightness differences in images collected under varying weather conditions, we have introduced an image brightness adaptive block. This block automatically adjusts the brightness of images during the data collection and processing stages, thereby reducing image disparities caused by weather variations. This step is crucial for improving the robustness of the model and ensuring accurate identification of agricultural diseases under different environmental conditions. Additionally, drawing inspiration from the Inception architecture and employing a flexible downsampling strategy, we have designed a custom inception block to integrate shallow and deep features effectively. To validate the effectiveness of our proposed approach, we conducted experiments using an agricultural disease image dataset processed with weather effects. The experimental results demonstrate that our model exhibits higher accuracy and robustness in agricultural disease image classification tasks compared to traditional methods. The code has been uploaded to GitHub at the following address: https://github.com/bettyaya/IBAC-Net.