Nano Drones for Precision Agriculture

Abstract

Fruit ies pose a signi cant threat to fruit industry. Fruit infestation has e ected the quality and quantity of our fruit export, reasulting in fruit ban and economical losses. Traditional methods used to guard against these enemies seem ine cient. Techniques like removing rotten fruits from the farm elds, using predators like ants and male sterilization are manual and resource demanding methods. Luring the ies using traps with honey, pleasant smell or di erent lights are passive techniques. In this work, we proposed the development of a lightweight acoustic sensory mechanism to be used on nano, low cost, drones against these fruit ies. We have demonstrated that audio data, captured from simple microphones, can be used to discriminate between helpful and harmful ies using machine learning. Additionally, the same audio data can be used to localize the ies. The two above mentioned methods can enable us to track the harmful ies. We have used our acoustic sensory mechanism in indoor and outdoor environments for the day and night experiments. Our nding of these experiments conclude that, if training and testing both are done in real time environment, classi cation accuracy will get better. For the drone experiments, we concluded that, due to low Signal to noise ratio, signal processing techniques are not enough for drone noise removal. We need some physical setup with light weight hurdles between audio sensing device and drone. With the use of these physical hurdles, we have improved our classi cation accuracy but this is not a very optimal setup for a drone to y with. We need noise damping drones, if we want to proceed with the idea of nano drones in future. For now, these audio sensing devices can be used in a form of an array to detect, classify and localize the fruit ies.