Comparison between Environmental Condition Parameters and Attack Degree of Vine using IoT Sensors System

Abstract

Vine disease identification using the IoT sensors network is a new method studied by many researchers. This method wants to replace the farmer’s work that uses classical methods to detect and prevent vine problems. The presented studies aim to compare the results of vine disease diagnosis obtained using classical and intelligent methods of data acquisition. Using the classical methods is identifying the degree of attack (DA) of the disease on the vine leaf. The environmental parameters are analyzed using neural network classification to identify the disease occurrence and to prevent them. So, the diagnosis system can be implemented using a supervised neural network in which the classes represent the DA identified on the leaves and the inputs are the atmospheric conditions.