Monitoring System for Autonomous Farming Drone based on Convolutional Neural Network

Abstract

The use of conventional tools that are still widely used by farmers in Indonesia can hinder the increase in agricultural productivity, affect the quality of crops and make production results inconsistent. In addition, farmers in Indonesia have not been able to utilise modern technology to support agricultural productivity due to a lack of insight into technology. To increase the efficiency of productivity in agriculture, one of the technologies often referred to as Unmanned Aerial Vehicles (UAV) or drones can be utilized in doing the work of farmers. The monitoring system allows farmers to know the condition of the drone, such as drones in abnormal conditions, too fast, not in accordance with the height and path that should be, and so on. There are eight classifications carried out using the Convolutional Neural Network (CNN) method so that the data accuracy is 97%.