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Agriculture is the foundation of human civilization. However, the rapidincrease of the global population poses a challenge on this cornerstone bydemanding more food. Modern autonomous greenhouses, equipped with sensors andactuators, provide a promising solution to the problem by empowering precisecontrol for high-efficient food production. However, the optimal control ofautonomous greenhouses is challenging, requiring decision-making based onhigh-dimensional sensory data, and the scaling of production is limited by thescarcity of labor capable of handling this task. With the advances ofartificial intelligence (AI), the internet of things (IoT), and cloud computingtechnologies, we are hopeful to provide a solution to automate and smartengreenhouse control to address the above challenges. In this paper, we propose asmart agriculture solution named iGrow, for autonomous greenhouse control(AGC): (1) for the first time, we formulate the AGC problem as a Markovdecision process (MDP) optimization problem; (2) we design a neuralnetwork-based simulator incorporated with the incremental mechanism to simulatethe complete planting process of an autonomous greenhouse, which provides atestbed for the optimization of control strategies; (3) we propose aclosed-loop bi-level optimization algorithm, which can dynamically re-optimizethe greenhouse control strategy with newly observed data during real-worldproduction. We not only conduct simulation experiments but also deploy iGrow inreal scenarios, and experimental results demonstrate the effectiveness andsuperiority of iGrow in autonomous greenhouse simulation and optimal control.Particularly, compelling results from the tomato pilot project in realautonomous greenhouses show that our solution significantly increases cropyield (+10.15\%) and net profit (+92.70\%) with statistical significancecompared to planting experts.

IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control