UPFormer: U-sharped Perception lightweight Transformer for segmentation of field grape leaf diseases

Abstract

In the smart agriculture community, segmentation models are de-facto for the timely detection and identification of plant diseases. However, the complex background and the small diseases make it challenging to segment the grape leaf disease. The performance improvement trend of the existing models comes at the cost of model size and computational cost, which hinders deployment on resource-constrained hardware. Towards this end, we propose a tailored lightweight segmentation architecture referred to as the U-sharped Perception Transformer (UPFormer) for field grape leaf diseases, which achieves a better trade-off between performance and efficiency. Concretely, we leverage the U-shaped hierarchy to acquire small tokens with superior cost efficiency. The prototype perception and the pixel-aware broadcast are developed in parallel architectures to learn low-frequency global information and mine high-frequency local information for small diseases in complex environments. Besides, a fast token aggregation recipe is designed to compensate for the sacrificed detail information in an efficient aggregation paradigm without increasing the count of parameters. Extensive experiments have demonstrated that UPFormer remarkably outperforms existing CNNs, ViTs, and CNN-Transformer hybrid architectures on the datasets, including Field-PV, Syn-PV, and Plant Village.