arXiv：cs.AI· Niko Carvajal Janke, Daoyuan Jin, Shivranjani Baruah, Nicholas Gunner, Jacob Maus, Yu Jiang, Kaitlin M. Gold·· 7 小时前AI 评分34
评估葡萄园田间研究中的人类-AI 协作工作流
Evaluating human-AI workflows for field research in viticulture
AI 导读
加州葡萄园精准病害控制项目测试了两套 human-AI 工作流：多智能体研究系统 Aleks v1 在 145 分钟内构建出 vine-scale 预测模型，在回溯模拟中巡视 45% 藤蔓位置时，将新记录红叶观测占比从 85.8% 提升至 94.1%。
正文
Abstract:We assessed the value of two live human-AI interactions in a precision disease control project in California vineyards. The project tested whether 2021-2024 commercial scouting records and remote-sensing measurements across 140 hectares could support 2025 red-leaf symptom forecasting for prioritized scouting and virus testing. In Workflow 1, Aleks v1, a multi-agent research system, developed forecasting models with iterative human refinement. We applied Aleks's 2024 vine-scale model to updated 2025 predictors and evaluated red-leaf forecasts against independent 2025 scouting. In retrospective simulations surveying 45% of all vine positions, adding model-informed row prioritization to adaptive scouting increased the encountered proportion of newly recorded red-leaf observations from 85.8% to 94.1%. Within-block scouting comparisons suggested the model mainly improved scouting allocation among blocks. Despite unreliable internal 2024 performance estimates from synthetic oversampling before train/test splitting, Aleks developed an informative vine-scale model in 145 minutes, increasing throughput and answering our research questions. In Workflow 2, we assessed whether higher model-score vines had more frequent virus detection, and whether Aleks could infer this sampling goal from a general prompt with data and literature. Aleks's plan prioritized balanced vineyard and model score coverage, while our plan prioritized field efficiency and high-model-score oversampling. Aleks's and our plans yielded 41/50 (82%) and 97/100 (97%) sampled vines. Aleks's plan omitted instructions for replacing missing vines, limiting implementation and operational value. Five of 137 sampled vines tested positive for grapevine red blotch virus (model score ROC AUC 0.735). These findings support assessing AI interactions by how well they advance field research objectives under live, project-specific constraints.
| Comments: | 35 pages, including supplementary materials. Supporting files S1-S4: this https URL |
| Subjects: | Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.07669 [cs.HC] |
| (or arXiv:2610.07669v1 [cs.HC] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07669 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Niko Carvajal Janke [view email]
[v1]
Tue, 6 Oct 2026 03:04:47 UTC (11,301 KB)
来源：arXiv：cs.AI · arxiv.org