Abstract
The present paper, introduces Adaptive Neuro Fuzzy Inference System (ANFIS) as one of the most mature and intelligent methods to predicte internal temperature and relative humidity of a greenhouse system. To conduct the application of the proposed strategy, an experimenntal greenhouse equipied with several sensors and actuators is engaged. In this sense a data base was collected during a period of day time where the temperature and relative humidity dynamics were observed inpresence of others climatic parameters and the actuators’ actions. The results demonstrate that by using ANFIS method, the predictions match the target points with a good accuracy. Therefore, the effectiveness of the strategy in term of both inside climate parameters’ prediction is guaranteed.
Highlights
Nowadays, the agriculture domain faces several challenges, especially in term of hard climate conditions and the water deficit for any outdoor cultivation type
The present paper aimed to investigate the prediction of internal temperature and internal humidity of the greenhouse as a complexe system, using both, Adaptive Neuro Fuzzy Inference System (ANFIS) strategy as an intelligent alternative and historical data for external climatic parameters and actuators’ operation regarding the Schefflera cultivation planted in the real greenhouse
For the same period of time, 2200 Data were collected for the humidity prediction case, As depicted in Figure 9, the training/ testing processes for the internal relative humidity data is similar to the temperature one in response to the same actuators and climatic variations for the other parameters, this time, the accuracy for the humidity prediction decreases compared to the temperature case as it is clearely seen from Figure 10, despite this, we can remark that the predicted internal relative humidity fits the observed one in an acceptable manner
Summary
The agriculture domain faces several challenges, especially in term of hard climate conditions and the water deficit for any outdoor cultivation type. It has been admitted that greenhouses take a leading role in facing such obstacles, greenhouses have the ability to maintain the adequate micro climate for cultivated indoor crops, in addition to shield those latests from any climate weather excess and unwanted pests and leaks [1]. From a modelisation point of view, greenhouses are considered to be nonlinear complex systems, the dynamic coupling between different parameters such as, the inside air temperature, the outside temperature, the air velocity, the relative humidity, in addition to the outdoor meteorological conditions, make the modelling and control of inside climate parameters more computationally difficult to be ensured. The first type is mostly based on mass/energy consevation principles and describes the systems from a physical knowledge, where a large set of parameters are included, resulted models are inacurate and the implemetation of such
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