An Automated Climate Control System for Greenhouse using Deep Learning to Minimize Tomato Crop Disease

Abstract

India is the agricultural country. The growth in the agriculture revenue is based upon the climate variables in the environments. Further, the growth of crop is decreased due to diseases. Today farmers face the major problem by crop diseases, uncontrolled environment and many diseases are reduced crop growth. The main reason for diseases is climate variables and the solution for this problem is minimizing the diseases using an automated greenhouse with six climate variables like Temperature, Air Humidity, Soil Moisture, pH Value, CO2, Light Intensity. The Proposed greenhouse system provides the solution for Tomato crop because tomato is a destructible crop, so, for the proposed system need to protect a tomato crop. The main aim of our project is minimizing the diseases as well as finding the list of pathogens using climate variables, therefore, we have been used Deep Neural System for finding the impact of climate variables inside the greenhouse and the deep learning provides a solution to the problem is better than the traditional classifier. Our proposed system is designed for training and testing with high performance.