LiteMixer: Cauliflower Disease Diagnosis based on a Novel Lightweight Neural Network

Abstract

Abstract Cauliflower, a globally cultivated and nutritionally rich crop, confronts significant challenges in quality and yield due to the rising prevalence of diseases. Traditional manual detection methods, suitable for empiricists or plant pathologists, prove inefficient. Furthermore, existing automated disease identification methods in cauliflower often neglect crucial computational performance metrics within computer vision algorithms, such as complexity, inference speed and training time. This study introduces LiteMixer, a novel lightweight model designed to address these challenges. The Lightweight Mixed-Domain Feature Extraction module (LMFE) meticulously captures global image features, followed by a maximum pooling layer that downscales the resulting multidimensional feature matrix. The Plug-and-Play Multi-Scale Lightweight Convolutional Attention Fusion module (MLCAF) integrates multichannel spatial features, connecting to fully connected layers for the final classification. Ablation experiments highlight the effectiveness of the LMFE module coupled with the MLCAF module. Comparative analyses against state-of-the-art and other lightweight models demonstrate LiteMixer achieving the highest accuracy in identifying cauliflower diseases at 99.86%. Notably, LiteMixer exhibits optimal computational performance, featuring minimal storage costs (4.02M) and the lowest parameter count, resulting in cost-effective computational expenses (16.78M). LiteMixer also boasts the fastest inference time (4.69 ms) and the shortest training time (865 s). This study positions LiteMixer as an advanced solution for diagnosing cauliflower leaf diseases in agricultural settings, underscoring its efficacy and practicality in overcoming the unique challenges associated with cauliflower disease detection within the realm of computer vision algorithms.