IFAC-PapersOnLine 49-16 (2016) 371–374 ScienceDirect ScienceDirect Available online at www.sciencedirect.com 2405-8963 © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2016.10.068 © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: fuzzy sensors, interpolation algorithms, microclimate monitoring. 1. INTRODUCTION The microclimate modeling and control in greenhouses it has been done measuring temperature and relative humi- dity, in some studies, it is considered physically indepen- dent between them, this neglect causes problems in the study of greenhouse microclimate. The problems involved in controlling greenhouses are strongly dependent on the geographical area; solutions that are valid in some regions must be adapted or changed in order to fit others. Until now, many of the controllers designed for greenhouses have been associated with a single control variable, i.e. tempe- rature, and this has given rise to monovariable controllers Herrero et al. (2007) and Fen (2010). The VPD has been implemented successfully, in Zolnier et al. (2000) it was reported a basis theoretical for defining operational constraints necessary to develop VPD con- troller, that research was to determine operational con- straints on equipment used for VPD control. In Vermeulen et al. (2012) is proposed automated leaf temperature mon-  This work was supported in part by the CONACYT-M´exico, by grant program: sabbatical stay abroad I0010-2014-02, under grant No. 246344. itoring of glasshouse tomato plants by using a leaf energy balance model, they measured VPD, it was determined by: H301 aspirated psychrometer Windspeed Ltd., Rhyl, Wales, UK., but these type sensor using the wet and dry bulb, a water reservoir and maintenance for over 7 days continuous. Unlike, in this work we use only two sensors: air temperature and relative humidity, embedded in a microcontroller, using interpolation algorithm fuzzy of the psychrometric chart. A methodology for irrigation control and nutrient supply was developed in Sigrimis et al. (2001), using common measurements of greenhouse climate, ambient and dry-bulb temperature, both mea- surements are converted to VPD using a virtual function from MACQU controller math library, it is not transparent the measurement of VPD if you not have the library. In Abdullah (2001), mean atmospheric vapor pressure deficit (AVPD) was used to develop a non stressed baseline equation and consequently the crop water stress index (CWSI), they computed instantaneous AVPD using the corresponding instantaneous wet and dry-bulb tempera- ture and the standard psychrometer equation List (1971), Howell et al. (1986). The VPD is very important climate variable, increases nonlinearly with increase in tempera- ture but decreases with increase in relative humidity, in Julio C´esar Ramos-Fern´andez * Jean-Fran¸coisBalmat ** Marco Antonio M´arquez-Vera * Fr´ed´eric Lafont ** Nathalie Pessel ** Eduardo Steed Espinoza-Quesada * * Universidad Polit´ecnica de Pachuca, Carr. Pachuca - Cd. Sahag´ un Km 20, Rancho Luna, Ex-Hacienda de Santa Barbara, C.P. 43830, Zempoala, Hgo. M´exico, Tel. (+52)771 5477510, (e-mail: jramos@ utt.edu.mx, marquez@ utt.edu.mx, steedeq@gmail.com). ** Aix Marseille Universit´e, Universit´e de Toulon, CNRS, ENSAM, LSIS UMR 7296, CS 60584 83041 Toulon Cedex 9 (e-mail: balmat@ univ-tln.fr, lafont@ univ-tln.fr, nathalie.pessel@ univ-tln.fr ) Abstract Instrumentation to measure microclimate in greenhouses is a task to developing before starting the identification and control moisture and temperature in greenhouses, a variable which best describe the microclimate and helps prevent fungus and disease risks, is the vapor pressure deficit (VPD), it is measured indirectly by means of two inputs: temperature and humidity, by means the psychrometric chart, and another way is using interpolation models. This paper shows methodology to measure VPD, by using fuzzy modeling type Takagi-Sugeno (T-S). Fuzzy modeling is based on expert and human knowledge, additionally this is combined with mathematical techniques to identify parameters of the antecedents and consequents of IF-THEN fuzzy rules, each IF-THEN fuzzy rule defines a submodel or region of interpolation. In this work, expert knowledge is the information described in psychrometric chart. Modeling learning was performed using air temperature and relative humidity, a set of 8 fuzzy rules was proposed to interpolate the VPD. The results of the fuzzy model are illustrated using measurements of an experimental greenhouse, and was contrasted with a model well known in the literature. The algorithm of fuzzy modelling VPD, was codified in two platform application real time: C to microcontroller and LabVIEW TM , thus it is possible measuring the instantaneous VPD. Fuzzy Modeling Vapor Pressure Deficit to Monitoring Microclimate in Greenhouses 