Greenhouse environmental monitoring and closed-loop control with crop growth model based on wireless sensors network. (January 2015)

• Record Type: Journal Article
• Authors: Yin, Jun; Yang, Yuwang; Cao, Hongxin; Zhang, Zhiyou
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 37:Number 1(2015)
• Page Start: 50
• Page End: 62
• Publication Date: 2015-01
• Subjects: DMS -- greenhouse -- tomato dynamic growth model -- WSN; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331214531006
• Languages: English
• ISSNs: 0142-3312

Abstract

Based on wireless sensor network (WSN) technology and crop growth simulation techniques, this paper shows the design and realization of an automatic monitoring and closed-loop control system in greenhouses. Firstly, a multi-hop network communication method based on clustering and simple medium access control that is suitable for the monitoring of a large-scale greenhouse environment is designed and analysed, and the simulation results show that its lifetime is 10% longer than LEACH (low energy adaptive clustering hierarchy) when 1% and 20% nodes die. Secondly, a physiological development day-based crop growth simulation model will be built to predict the tomato growth and make further decisions in adjusting the greenhouse climate. In order to obtain the model indicators, early experiments were carried out on four kinds of tomato variety, and the experiment results show that the proposed model has a higher accuracy than the effective temperature model on the root mean square error within 1–4 days, and on the mean absolute error within 2–4 days. Finally, according to the proposed methods, a comprehensive greenhouse dynamic monitoring and closed-loop control system with a 60 MC13213 nodes WSN was implemented. The implementation results show that with three AAA Ni–MH (nominal capacity 750 mAh) batteries, 80% nodes maintained a survival time of 45–60 days, and the model prediction compared with the observed value is at a high correlation efficient of 95%.