Real time weed identification with enhanced mobilevit model for mobile devices.

Deep learning model optimization have notably enhanced weed identification accuracy. However, there is a short-fall in detailed research on optimizing models for weed identification with images from mobile embedded systems. Also, existing methods generally use large, slow multi-layer convolutional networks (CNNs), which are impractical for use on mobile embedded devices. To address those issues, we propose a lightweight weed identification model based on an enhanced MobileViT architecture, effectively balancing high accuracy with real-time performance. Our approach begins with the application of a multi-scale retinal enhancement algorithm featuring color restoration to preprocess image data. This step improves the clarity of images, particularly those with blurred edges or significant shadow interference. Following this, we introduce an optimized MobileViT model that incorporates the Efficient Channel Attention (ECA) module into the weed feature extraction network. This design ensures robust feature extraction capabilities while simultaneously reducing the model's parameters and computational complexity. The MobileViT model within our feature extraction network is engineered to concurrently learn local and global semantic information. This capability allows it to accurately distinguish subtle differences between weeds and crops by leveraging a minimal number of modules. To demonstrate the effectiveness of our model, it achieved an F1 score of 98.51% and an average identification time of 89 milliseconds per image. These results underscore its suitability for lightweight deployment, maintaining high accuracy while minimizing model complexity.