NDLI: A Neural Network Model to Control Greenhouse Environment

Content Provider	IEEE Xplore Digital Library
Author	Salazar, R. Lopez, I. Rojano, A.
Copyright Year	2007
Description	Author affiliation: Univ. Autonoma Chapingo, Chapingo (Salazar, R.; Lopez, I.; Rojano, A.)
Abstract	This research was developed in a greenhouse located in Mexico, in which there are big variations in temperature and relative humidity, generating production losses. Consequently a good greenhouse control tool was necessary to keep these variables inside of the optimal levels. Black box models have been applied in this greenhouse to predict temperature and relative humidity, however they fail in relative humidity predictions because of non linear relationships in the variables. Therefore an Artificial Neural Network (ANN) was implemented because it excel at uncovering patterns or relationships in data and it is also a powerful non-linear estimator. A total number of 14,490 data patterns were available 50% for training, 25% for verification, and 25% for testing. The ANN developed demonstrates a highly accurate estimation for both variables which can be used to forecast the conditions inside of the greenhouse and consequently take actions ahead of time, avoiding economical losses.
Starting Page	311
Ending Page	318
File Size	225478
Page Count	8
File Format	PDF
ISBN	9780769531243
DOI	10.1109/MICAI.2007.33
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-04
Publisher Place	Mexico
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Temperature relative humidity Artificial neural networks Power generation economics Predictive models greenhouse Neural networks Optimal control Humidity Production temperature Economic forecasting Testing
Content Type	Text
Resource Type	Article

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