Transformer-Based Weed Segmentation for Grass Management

Weed control is among the most challenging issues for crop cultivation and turf grass management. In addition to hosting various insects and plant pathogens, weeds compete with crop for nutrients, water and sunlight. This results in problems such as the loss of crop yield, the contamination of food crops and disruption in the field aesthetics and practicality. Therefore, effective and efficient weed detection and mapping methods are indispensable. Deep learning (DL) techniques for the rapid recognition and localization of objects from images or videos have shown promising results in various areas of interest, including the agricultural sector. Attention-based Transformer models are a promising alternative to traditional constitutional neural networks (CNNs) and offer state-of-the-art results for multiple tasks in the natural language processing (NLP) domain. To this end, we exploited these models to address the aforementioned weed detection problem with potential applications in automated robots. Our weed dataset comprised of 1006 images for 10 weed classes, which allowed us to develop deep learning-based semantic segmentation models for the localization of these weed classes. The dataset was further augmented to cater for the need of a large sample set of the Transformer models. A study was conducted to evaluate the results of three types of Transformer architectures, which included Swin Transformer, SegFormer and Segmenter, on the dataset, with SegFormer achieving final Mean Accuracy (mAcc) and Mean Intersection of Union (mIoU) of 75.18% and 65.74%, while also being the least computationally expensive, with just 3.7 M parameters.

Automated summary

Weed control is a challenging issue that affects crop yield and field aesthetics. Deep learning techniques, such as attention-based Transformer models, have shown promising results in weed detection. In this study, the SegFormer architecture achieved high accuracy with a relatively low computational cost for weed localization.