Artificial Neural Network - Based Greenhouse Monitoring

This paper evaluates the applications of ANN's which are abbreviated as Artificial Neural Networks in greenhouse technology, which explains the development in upcoming years by adjusting new technologies like Machine Learning(ML) and the Internet of Things (IoT). This paperwork is analyzed using feed-forward architecture, while hybrid networks and recurrent are tiny make use of various greenhouse tasks. In this research, we presented many training techniques for different networks; practicality using different optimization models for the learning process is exhibited. Different applications of Neural Networks' many advantages and disadvantages were observed. Based on energy expenditure, the prediction of a microclimate to many specific tasks like control of carbon dioxide. The most supreme in this work might be used as some of the suggestions for developers who utilize smart protected agriculture technology, in which system demand technologies 4.0.