A SMART DRONE FOR ENSURING PRECISION AGRICULTURE WITH ARTIFICIAL NEURAL NETWORK

Abstract

The smartness in agriculture can be built through incorporating technologies like wireless sensor network, cloud computing, artificial intelligence, big data analytics and internet of things (IoT). Field/Crop management, irrigation control, water management and nutrient management are the few solutions that driving the smart farming. The unmanned aerial vehicles (UAVs) facilitate the field and crop monitoring solutions without any environmental disturbs. The drones in correlation with crop management and field analysis, estimates the factors like chlorophyll content, leaf variation index and vegetation index to make a decision about the healthiness of the plant as well as land. The proposed approach is planned to yield accurate, cost-effective and multipurpose drone into the place. The imaging techniques of camera and its efficiency are inherently analyzed and applied in design to make accurate and timely decision. Further the images received from the sensors are processed with artificial neural network models to optimize the performance of the system. By feeding intelligence, the spraying task of the drone can be automated. The efficiency of the proposed intelligent model is evaluated against most popular artificial neural network (ANN) algorithms experimented in existing works like naïve bayes, K-nearest neighbor (KNN), support vector machine(SVM), decision tree and random forest.