An Automated Climate Control System for Greenhouse Using Deep Learning for Tomato Crop

Abstract

The agricultural growth or crop growth depends on the climate variables in environment. Due to diseases, there is decreasing in crop growth. An Automated Greenhouse is an important factor in the crop growth of agriculture. We consider the six climate variables for the greenhouse, i.e. Temperature, Humidity, Soil Moisture, CO2 Concentration, Light Intensity and pH scale for Tomato crop. Tomato is an economically the important vegetable crop on the world. The rules and regulation of tomato crop environment and production of the greenhouse are difficult and to minimize these difficulties. For that difficulties to identify a problem first and provide the solution as quickly as possible. The main difficulty in the tomato crop is diseases. The aim of our project is to find out the pathogens of diseases using the climate variables. To finding the impact of climate variable, we use Deep Neural Network System. Because of DNN system shown the outstanding performance compared to traditional machine learning. The Deep Neural Network is used to design system that can be trained and test with high performance