10th International Congress on Information and Communication Technology in concurrent with ICT Excellence Awards (ICICT 2025) will be held at London, United Kingdom | February 18 - 21 2025.
Authors - Abdellah Islam Kafi, Antonio P. Sanfilippo, Raka Jovanovic, Sa’d Abdel-Halim Shannak Abstract - In this paper, we develop a drone-based solution for detecting productivity characteristics of tomato crops inside agricultural greenhouses using the YOLO8 computer vision model; a mobile phone is used to deploy the trained model. The implementation leverages the Apple Neural Engine (NE), a hardware accelerator module embedded in recent Apple mobile phones, to enable fast and efficient inference. Our video acquisition component also employs a DJI remote controller that streams live video from the drone to the mobile app for processing. The main objective is to perform rapid and precise detection of tomatoes within greenhouses, where drones can improve efficiency and coverage. We describe the model architecture and various optimization techniques suitable for embedded-platform deployment. The experimental study demonstrates the system’s effectiveness in detection accuracy and inference time when utilizing NE compared to CPU-based inference. We also compare accuracy, model size, and inference speed across variants of the YOLO algorithm.
