Abstract-- Smart farming and precision agriculture represent game-changer technologies for efficient and sustainable agribusiness. Miniaturized palm-sized drones can act as flexible smart sensors inspecting crops, looking for early signs of potential pest outbreaking. However, achieving such an ambitious goal requires hardware-software codesign to develop accurate deep learning (DL) detection models while keeping memory and computational needs under an ultra-tight budget, i.e., a few MB on-chip memory and a few 100s mW power envelope. This work presents a novel vertically integrated solution featuring two ultra-low power System-on-Chips (SoCs), i.e., the dual-core STM32H74 and a multi-core GWT GAP9, running two Stateof-the-Art DL models for detecting the Popillia japonica bug. Despite the paramount improvement compared to A crucial aspect of any modern farming activity is a human-operated traps, these State-of-the-Art (SoA) solutions precise and timely intervention in the case of pest (insect) still require costly external infrastructure, i.e., mainframes infestations to minimize the production/economic damage and servers, and need high-throughput radio connectivity and the environmental impact of the required treatments [1].