Recent technological advances have paved the way for remote agricultural sensing and automation. Sophisticated low-cost sensors and communications systems can be used as IoT components to monitor and control systems for a sustainable and healthy environment.

Nowadays, Machine Learning algorithms are widely used in many fields and are particularly innovative in agriculture to compute tasks such as species recognition, water management, crop quality, disease detection, and weed detection.

The project is focused on an automatic method for monitoring parasite insects from images taken in pest traps. The Codling Moth is a particular insect that looks like a butterfly, and it is a dangerous parasite for apple fruit crops.

An energy efficient IoT solution must show how it is possible to classify parasites from other general insects using low-cost IoT hardware.
It will execute machine learning algorithms directly on IoT device and classifies if the insect captured by the camera is a Codling Moth to be reported for an immediate counteraction.