WeevilNet
Project Overview
Year
About
WeevilNet is an AI-based bio-acoustic surveillance system that detects red palm weevil infestations before irreversible damage occurs. It converts subtle larval boring sounds into Mel spectrograms, which are analysed by a deep learning model achieving up to 98.2% accuracy. The system includes robust signal processing, a production-ready neural network, and a user-friendly dashboard for farmers. By enabling precise, non-invasive detection, WeevilNet reduces pesticide use, protects crops, and supports sustainable agriculture in vulnerable regions
Team
Mennat Allah Hassan
Egypt University of Informatics
Mennat Allah Hassan is a PhD student in Artificial Intelligence with a Master’s degree and expertise in Computer Vision. She began her career at Google Dubai in Android development before moving into VR/AR. With nearly 8 years of teaching, she has instructed in Mobile and Game Development, Cyber-Security, and Cryptography at universities and platforms like Udacity. She has supervised 10+ graduation projects in AI, Computer Vision, and VR/AR, resulting in publications and awards such as DELL EMC Envision. She also contributed to two Guinness World Record events, including being a top ten winner at the 2018 Haji Hackathon.
Nader Fayed
Egypt University of Informatics
Nader Fayed is an award-winning student and innovator at the intersection of Artificial Intelligence, quantum computing, and sustainable energy. Recognized at Cairo Innovates and the World Robot Olympiad, he has advanced projects in CO₂ capture, hydrogen production, and AI-driven knowledge systems. As a public speaker and IT specialist, Nader has represented youth at COP27 and the World Urban Forum. With a passion for mentorship and global impact, he continues to bridge science, technology, and community advancement.
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