Toward Smart Palm Precision Agriculture: A Study on Palm Tree and Red Palm Weevil Detection

In the era of precision agriculture, where a 70% increase in global food production is imperative,this research unfolds as a transformative force propelled by data-driven methodologies.Focusing on the vital realm of palm cultivation, which is particularly crucial for date palmproduction and environmental balance, this study tackles the challenges posed by diverseand voluminous data through the integration of remote sensing big data and the Internet ofThings (IoT). The central stage is deep learning, ushering in a new era of smart precisionagriculture tailored for effective palm management. Three key challenges were addressed:agricultural data management, palm tree detection and counting, and pest and disease management,all with the overarching goal of fortifying resilience, productivity, and sustainabilityin palm production. The contributions of this research are manifested in a scalable remotesensing data management model, the introduction of a distributed architecture to handlemassive, high-resolution remote sensing data, and a deep learning and UAV-based approachfor efficient palm tree detection. This revolutionary approach not only accelerates data collection,reduces errors, and enhances decision-making but also contributes significantly to thesustainability of the palm industry and aligns with Sustainable Development Goals (SDGs).Additionally, this study presents an innovative solution for sustainable palm cultivation byintegrating computer vision, deep learning, IoT, and geospatial data for the early detectionand mapping of Red Palm Weevil (RPW) infestations. Achieving 98.8%-99.5% accuracy anddetection rate with a custom DL model, this technology-driven strategy enables comprehensivemapping, monitoring, and targeted management of RPW spread, benefiting agriculturalagencies, growers, and researchers.