Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for
  Early Detection and Mapping of Red Palm Weevil
Yosra Hajjaji Ayyub Alzahem Wadii Boulila Imed Riadh Farah Anis Koubaa
The Red Palm Weevil (RPW) is a highly destructive insect causing economic
losses and impacting palm tree farming worldwide. This paper proposes an
innovative approach for sustainable palm tree farming by utilizing advanced
technologies for the early detection and management of RPW. Our approach
combines computer vision, deep learning (DL), the Internet of Things (IoT), and
geospatial data to detect and classify RPW-infested palm trees effectively. The
main phases include; (1) DL classification using sound data from IoT devices,
(2) palm tree detection using YOLOv8 on UAV images, and (3) RPW mapping using
geospatial data. Our custom DL model achieves 100% precision and recall in
detecting and localizing infested palm trees. Integrating geospatial data
enables the creation of a comprehensive RPW distribution map for efficient
monitoring and targeted management strategies. This technology-driven approach
benefits agricultural authorities, farmers, and researchers in managing RPW
infestations and safeguarding palm tree plantations' productivity.