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

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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 palm production and environmental balance, this study tackles the challenges posed by diverse and voluminous data through the integration of remote sensing big data and the Internet of Things (IoT). The central stage is deep learning, ushering in a new era of smart precision agriculture 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 sustainability in palm production. The contributions of this research are manifested in a scalable remote sensing data management model, the introduction of a distributed architecture to handle massive, high-resolution remote sensing data, and a deep learning and UAV-based approach for efficient palm tree detection. This revolutionary approach not only accelerates data collection, reduces errors, and enhances decision-making but also contributes significantly to the sustainability of the palm industry and aligns with Sustainable Development Goals (SDGs). Additionally, this study presents an innovative solution for sustainable palm cultivation by integrating computer vision, deep learning, IoT, and geospatial data for the early detection and mapping of Red Palm Weevil (RPW) infestations. Achieving 98.8%-99.5% accuracy and detection rate with a custom DL model, this technology-driven strategy enables comprehensive mapping, monitoring, and targeted management of RPW spread, benefiting agricultural agencies, growers, and researchers.

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