Identification of Tea Disease Under Complex Backgrounds Based on Minimalism Neural Network Architecture and Channel Reconstruction Unit

Abstract

Tea production plays a crucial role in maintaining agricultural output, and the prompt diagnosis and efficient management of tea diseases are essential for ensuring a healthy tea industry. Traditional machine learning techniques for disease identification often require time-consuming feature engineering tasks, which can be a bottleneck in achieving accurate and efficient results. In contrast, deep learning approaches have shown superior performance in disease identification by eliminating the need for manual feature engineering. However, in complex backgrounds, such as when environmental variables and multi-scale changes interact with the imaging of tea diseases, feature extraction becomes extremely challenging. In this study, a novel technique for tea diseases recognition in a complex setting is proposed, including the channel reconstruction unit (CRU) and the minimalism neural network model VanillaNet, named VCRUNet, to address the aforementioned issues. VCRUNet incorporates the channel reconstruction unit (CRU), which effectively reduces channel redundancy between features in the convoluted neural network, thereby improving the model's ability to extract relevant features. To overcome the limitations of limited sample data, we fine-tuned the model parameters using transfer learning on a self-built tea disease dataset, supplemented with the Plant Village dataset for pre-training. The experimental results demonstrate that our proposed technique achieves an impressive accuracy of 92.48% in accurately detecting tea diseases in complex environments. This significant improvement in accuracy outperforms current methods and enhances the efficacy of tea disease identification. Meanwhile, the detection speed is 4.5 seconds per 100 images. The outcomes of this research have a direct impact on the early diagnosis and effective management of tea diseases. By providing a more accurate and efficient approach, our technique contributes to the overall agricultural output and promotes the long-term expansion of the tea industry.INDEX TERMS Tea diseases, image recognition, minimal neural network model, channel reconstruction unit, transfer learning.