用机器学习自动调控温室，平衡作物收益与风险。
Towards Fully Automated Decision-Making Systems for Greenhouse Control: Challenges and Opportunities
- 基于环境观测自动优化决策策略，实现闭环控制。
- 在46支队伍中排名第二，验证了方法有效性。
- 适合农业自动化、智能温室研究者参考。
机器学习已在游戏、机器人等多个领域成功构建控制策略，通过从环境观测中自动优化策略参数，生成最优决策序列。本文聚焦农业这一独特且实际的应用场景——温室管理，探讨如何及时做出关键决策（如供水、加热），在降低植物损伤风险的同时最大化作物收益。我们综述了该领域的最新研究，识别出特定挑战与潜在解决方案，并提出未来研究方向。此外，以我们在第三届自主温室挑战赛中排名第二的实践为例，分析设计自主农场管理系统的关键考量，为构建高效智能农业系统提供经验借鉴。
原文摘要 · Abstract (English)
Machine learning has been successful in building control policies to drive a complex system to desired states in various applications (e.g. games, robotics, etc.). To be specific, a number of parameters of policy can be automatically optimized from the observations of environment to be able to generate a sequence of decisions leading to the best performance. In this survey paper, we particularly explore such policy-learning techniques for another unique, practical use-case scenario--farming, in which critical decisions (e.g., water supply, heating, etc.) must be made in a timely manner to minimize risks (e.g., damage to plants) while maximizing the revenue (e.g., healthy crops) in the end. We first provide a broad overview of latest studies on it to identify not only domain-specific challenges but opportunities with potential solutions, some of which are suggested as promising directions for future research. Also, we then introduce our successful approach to being ranked second among 46 teams at the ''3rd Autonomous Greenhouse Challenge'' to use this specific example to discuss the lessons learned about important considerations for design to create autonomous farm-management systems.
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