  Artificial intelligence has smoothly penetrated several economic activities,
especially monitoring and control applications, including the agriculture
sector. However, research efforts toward low-power sensing devices with fully
functional machine learning (ML) on-board are still fragmented and limited in
smart farming. Biotic stress is one of the primary causes of crop yield
reduction. With the development of deep learning in computer vision technology,
autonomous detection of pest infestation through images has become an important
research direction for timely crop disease diagnosis. This paper presents an
embedded system enhanced with ML functionalities, ensuring continuous detection
of pest infestation inside fruit orchards. The embedded solution is based on a
low-power embedded sensing system along with a Neural Accelerator able to
capture and process images inside common pheromone-based traps. Three different
ML algorithms have been trained and deployed, highlighting the capabilities of
the platform. Moreover, the proposed approach guarantees an extended battery
life thanks to the integration of energy harvesting functionalities. Results
show how it is possible to automate the task of pest infestation for unlimited
time without the farmer's intervention.

Machine Learning;Computer Vision and Pattern Recognition