Proactive monitoring and analysis of technological processes of growing crops in automated greenhouses

Abstract

Agricultural automation can reduce time and cost of crop production and minimize human factor that can lead to crop damage. This paper focuses on automating crop growth in compact greenhouses that automate several technological processes including periodic irrigation with a nutrient solution and a biofilter to ensure cyclic cultivation, measuring temperature, humidity, etc. Machine learning methods help estimate and predict operation parameters. During the experiment, the optimal methods and parameters were determined, and the best prediction accuracy could be achieved using the random forest method. Use of this approach enables proactive control of technological processes, ensures compliance with growing regulations and results in resources economy. Future research will develop a formal method for proactive process control.