A Theoretical Framework of Smart System Modelling of Crop Based on GAN and IoT Platform

The advent of the communication and cutting edge technologies is playing a key role in reforming the various sectors and so as in the field of the agronomy specifically in precision agriculture (PAg). Greenhouses facing the problem of barren land basically need such systems potentially relevant to control re-circulation in the optimal way. In this paper the proposed system is segregated into three parts cyber physical system (CPS), edge computing and cloud computing-gan layers with use of IOT protocols like message queuing telemetry transport (MQTT) protocol or constrained application protocol (CoAP) for communication purpose [3]. This paper describes the theoretically concept of using GAN based on the two multilayer perceptron models generator and discriminator. GAN uses the analyzed data from the cloud part and then generator generates the data distribution (probability distribution). The discriminator discriminates the data as per the certain system requirement to enable more automation needed for crops growth. GAN part works on the min max concept in which discriminator is trained to maximize the level assignment probability whereas generator is getting trained to minimize the chances of mistakes done by discriminator to support more accurate result. The concept of the GAN will enable the system to enhance its intelligence in order to make more sharpen and accurate decision.