Internet of Things-Based Crop Classification Model Using Deep Learning for Indirect Solar Drying

The energy which is obtained from the sun in the form of light and heat is known as solar energy. Nowadays, technology is focused on utilization of this rich resource of energy. So many ways to harness and utilize this source of energy have already been introduced; solar drying of crops is one of its applications. Drying of crops is required to improve the quality of the crops as well as to protect crops from so many unwanted issues like moisture, pest/insect attacks, and birds/animals. Traditional methods are still used to dry the crops; drying of crops is required to preserving food product for long time. In this paper, we studied about the working mechanism of indirect solar dryers and introduced IoT-based system to control and monitor the temperature of the solar dryer as per the requirement of specific crop. To achieve the automation accurately and precisely deep learning method is also used to set the required temperature according to the requirement of the specific crop.