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
Nader Fayed
Dr. Hoda M. O. Mokhtar
Mennat Allah Hassan
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.
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