HARVEST-Vision: International Workshop on Applications of CV and HPC in Agriculture

The rapid growth of computer vision (CV) and artificial intelligence (AI) is reshaping agriculture, offering new approaches to challenges in food security, climate resilience, and sustainability. Scaling these technologies requires bridging core CV research with domain-specific data infrastructures and practical user needs. Key applications include crop and soil monitoring, pest and disease detection, yield forecasting, and resource optimization. Yet, these tasks are hindered by heterogeneous field data, limited labeled datasets—especially in edge environments—and the demand for models that are both scalable and interpretable.Recognizing these challenges, we launched the HARVEST workshop series (the first hosted at 54th International Conference on Parallel Processing in San Diego, CA, supported by the NSF ICICLE AI Institute led by the Ohio State University) to build community and cyberinfrastructure at the intersection of AI, HPC, and agriculture. The event showcased high-profile keynote speakers, and engaged around 30 participants from academia, national labs, and industry. Our agenda included technical talks, panels, and hands-on demonstrations. Through a partnership with NSF AI Institutes Virtual Organization, we were able to secure travel awards for 12 rising academic researchers, many of whom are now part of a growing expert network community of computer scientists and agricultural experts.Bringing this momentum to WACV, our HARVEST-Vision workshop directly aligns with the community’s focus on impactful real-world CV: agricultural cyberinfrastructure is an urgent, societally relevant application space for advances in robust perception, domain adaptation, scalable datasets, and explainable, deployable AI. By fostering interdisciplinary collaborations, catalyzing new datasets, benchmarks, and reproducible pipelines, and supporting diversity via travel awards, our workshop will expand WACV’s reach, accelerating knowledge transfer and enabling high-impact computer vision research for agriculture and beyond.