Prediction of factors for Controlling of Green House Farming with Fuzzy based multiclass Support Vector Machine

Abstract

A smart greenhouse is a scheme with the aim of allowing the plant growth optimally due to the control in temperature, soil moisture, and humidity. It is necessary to go for best possible smart greenhouse controlling system to maneuver the parameters in accordance with the climatic and seasonal changes plant. In this proposed work, the appropriate setting, monitoring of the model parameters like optimal temperature, humidity, and soil moisture in the greenhouse farm available at Modakkurichi near Erode is carried out using fuzzy logic and multiclass Support Vector Machine (SVM) techniques. It is efficiently implemented as the fuzzy trapezoidal membership function for each sample within the hyper-sphere as a linear function of the selected sample’s distance in the non-linear SVM hyperplane. Based on the simulation and experimental results, the fuzzy logic with Multi-class Support Vector Machine method is effective in selection of the rules to make decision. Simulation of the work was carried out in Python using the open source tool COLAB. Statistical parameters are evaluated for the proposed Fuzzy based SVM method. Root Mean Square Error of the proposed controller has the minimum value as 0.962E-02 when compared to other controllers which vary from 1.04 E-02 to 8.27 E-02.

Keywords

• Greenhouse
• Climate
• Machine learning
• Vegetables
• Membership
• Kernel