Greenhouse Micro-Climate Prediction Based on Fixed Sensor Placements: A Machine Learning Approach

Abstract

Accurate measurement of micro-climates that include temperature and relative humidity is the bedrock of the control and management of plant life in protected cultivation systems. Hence, the use of a large number of sensors distributed within the greenhouse or mobile sensors that can be moved from one location to another has been proposed, which are both capital and labor-intensive. On the contrary, accurate measurement of micro-climates can be achieved through the identification of the optimal number of sensors and their optimal locations, whose measurements are representative of the micro-climate in the entire greenhouse. However, given the number of sensors, their optimal locations are proven to vary from time to time as the outdoor weather conditions change. Therefore, regularly shifting the sensors to their optimal locations with the change in outdoor conditions is cost-intensive and may not be appropriate. In this paper, a framework based on the dense neural network (DNN) is proposed to predict the measurements (temperature and humidity) corresponding to the optimal sensor locations, which vary relative to the outdoor weather, using the measurements from sensors whose locations are fixed. The employed framework demonstrates a very high correlation between the true and predicted values with an average coefficient value of 0.91 and 0.85 for both temperature and humidity, respectively. In other words, through a combination of the optimal number of fixed sensors and DNN architecture that performs multi-channel regression, we estimate the micro-climate of the greenhouse.