Neural networks for Pest Detection in Precision Agriculture

Apple is one of the most produced fruit crops in the world. Recent advances in Artificial Intelligence and the Internet of Things can reduce production costs and improve crop quality by providing prompt detection of dangerous parasites. This paper presents an effective solution to automate the detection of the Codling Moths. The system takes pictures of trapped insects in the orchard, analyzes them through a DNN algorithm, and sends alarms to the farmer in case of a positive detection. The system is fully autonomous and can operate unattended for the entire crop season. Detection reports are used for optimizing the treatment with chemicals only when threats are identified. The prototype is designed with an embedded platform powered by a small solar panel to achieve an energy-neutral balance.