A Simulator-based Planning Framework for Optimizing Autonomous Greenhouse Control Strategy
Keywords:Description And Modeling Of Novel Application Domains, Industry / Application Challenge Problems, Integration Of Multiple Planning And Scheduling Techniques, Or Of Planning And Scheduling Techniques With Techniques From Other Areas Or Disciplines
AbstractThe rapidly growing global population presents challenges and demands for efficient production of healthy fresh food. Autonomous greenhouse equipped with standard sensors and actuators (such as heating and lighting) which enables control of indoor climate for crop production, contributes to producing higher yields. However, it requires skilled and expensive labor, as well as a large amount of energy. An autonomous greenhouse control strategy, powered by AI algorithms by optimizing the yields and resource use simultaneously, offers an ideal solution to the dilemma. In this paper, we propose a two-stage planning framework to automatically optimize greenhouse control setpoints given specific outside weather conditions. Firstly, we take advantage of cumulative planting data and horticulture knowledge to build a multi-modular simulator using neural networks, to simulate climate change and crop growth in the greenhouse. Secondly, two AI algorithms (reinforcement learning and heuristic algorithm) as planning methods are applied to obtain optimal control strategies based on the simulator. We evaluate our framework on a cherry-tomato planting dataset and demonstrate that the simulator is able to simulate greenhouse planting processes with high accuracy and fast speed. Moreover, the control strategies produced by the AI algorithms all obtain superhuman performance, in particular, significantly outperform all teams of the second “Autonomous Greenhouse Challenge” in terms of net profits.
How to Cite
An, Z., Cao, X., Yao, Y., Zhang, W., Li, L., Wang, Y., Guo, S., & Luo, D. (2021). A Simulator-based Planning Framework for Optimizing Autonomous Greenhouse Control Strategy. Proceedings of the International Conference on Automated Planning and Scheduling, 31(1), 436-444. https://doi.org/10.1609/icaps.v31i1.15989
Novel Applications Track