Farmers rely on in-field observations to make well-informed crop management decisions to maximize profit and minimize adverse environmental impact. However, obtaining real-world crop state measurements is labor-intensive, time-consuming and expensive. In most cases, it is not feasible to gather crop state measurements before every decision moment. Moreover, in previous research pertaining to farm management optimization, these observations are often assumed to be readily available without any cost, which is unrealistic. Hence, enabling optimization without the need to have temporally complete crop state observations is important. An approach to that problem is to include measuring as part of decision making. As a solution, we apply reinforcement learning (RL) to recommend opportune moments to simultaneously measure crop features and apply nitrogen fertilizer. With realistic considerations, we design an RL environment with explicit crop feature measuring costs. While balancing costs, we find that an RL agent, trained with recurrent PPO, discovers adaptive measuring policies that follow critical crop development stages, with results aligned by what domain experts would consider a sensible approach. Our results highlight the importance of measuring when crop feature measurements are not readily available.

翻译：农户依赖田间观测来制定明智的作物管理决策，以实现利润最大化并减少对环境的不利影响。然而，获取实际作物状态测量值需要耗费大量人力、时间且成本高昂。在大多数情况下，无法在每个决策时刻前都收集作物状态测量数据。此外，在以往关于农场管理优化的研究中，这些观测值常被假定为无需任何成本即可获得，这并不符合实际情况。因此，实现无需时间上完整的作物状态观测即可进行优化至关重要。解决该问题的一种方法是将测量纳入决策过程。作为解决方案，我们应用强化学习（RL）来推荐同时测量作物特征和施用氮肥的适当时机。基于现实考量，我们设计了一个包含明确作物特征测量成本的RL环境。在平衡成本的同时，我们发现采用循环PPO训练的RL智能体能够发现遵循关键作物生长阶段的自适应测量策略，其结果与领域专家认为合理的方法相符。我们的研究结果突显了在作物特征测量数据不易获取时进行测量决策的重要性。