  Agriculture is the foundation of human civilization. However, the rapid
increase of the global population poses a challenge on this cornerstone by
demanding more food. Modern autonomous greenhouses, equipped with sensors and
actuators, provide a promising solution to the problem by empowering precise
control for high-efficient food production. However, the optimal control of
autonomous greenhouses is challenging, requiring decision-making based on
high-dimensional sensory data, and the scaling of production is limited by the
scarcity of labor capable of handling this task. With the advances of
artificial intelligence (AI), the internet of things (IoT), and cloud computing
technologies, we are hopeful to provide a solution to automate and smarten
greenhouse control to address the above challenges. In this paper, we propose a
smart agriculture solution named iGrow, for autonomous greenhouse control
(AGC): (1) for the first time, we formulate the AGC problem as a Markov
decision process (MDP) optimization problem; (2) we design a neural
network-based simulator incorporated with the incremental mechanism to simulate
the complete planting process of an autonomous greenhouse, which provides a
testbed for the optimization of control strategies; (3) we propose a
closed-loop bi-level optimization algorithm, which can dynamically re-optimize
the greenhouse control strategy with newly observed data during real-world
production. We not only conduct simulation experiments but also deploy iGrow in
real scenarios, and experimental results demonstrate the effectiveness and
superiority of iGrow in autonomous greenhouse simulation and optimal control.
Particularly, compelling results from the tomato pilot project in real
autonomous greenhouses show that our solution significantly increases crop
yield (+10.15\%) and net profit (+92.70\%) with statistical significance
compared to planting experts.
