Domain Generalization for Crop Segmentation with Standardized Ensemble Knowledge Distillation

Simone Angarano, Mauro Martini, Alessandro Navone, Marcello Chiaberge; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5450-5459

In recent years precision agriculture has gradually oriented farming closer to automation processes to support all the activities related to field management. Service robotics plays a predominant role in this evolution by deploying autonomous agents that can navigate fields while performing tasks such as monitoring spraying and harvesting without human intervention. To execute these precise actions mobile robots need a real-time perception system that understands their surroundings and identifies their targets in the wild. Existing methods however often fall short in generalizing to new crops and environmental conditions. This limit is critical for practical applications where labeled samples are rarely available. In this paper we investigate the problem of crop segmentation and propose a novel approach to enhance domain generalization using knowledge distillation. In the proposed framework we transfer knowledge from a standardized ensemble of models individually trained on source domains to a student model that can adapt to unseen realistic scenarios. To support the proposed method we present a synthetic multi-domain dataset for crop segmentation containing plants of variegate species and covering different terrain styles weather conditions and light scenarios for more than 70000 samples. We demonstrate significant improvements in performance over state-of-the-art methods and superior sim-to-real generalization. Our approach provides a promising solution for domain generalization in crop segmentation and has the potential to enhance a wide variety of agriculture applications.