Sustainable Palm Tree Farming: Leveraging IoT and Multi-Modal Data for Early Detection and Mapping of Red Palm Weevil

TL;DR

This paper presents an integrated IoT, deep learning, and geospatial data system for early detection and mapping of Red Palm Weevil infestations, enhancing sustainable palm farming practices.

Contribution

It introduces a novel multi-modal approach combining sound analysis, UAV imagery, and geospatial data for precise RPW detection and mapping.

Findings

DL model achieves 100% precision and recall in RPW detection

Effective mapping of RPW distribution for targeted management

Integration of technologies improves early detection accuracy

Abstract

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.