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.

翻译：红棕象甲（RPW）是一种高破坏性昆虫，在全球范围内造成经济损失并影响棕榈树种植。本文提出一种创新的可持续棕榈树种植方法，通过利用先进技术实现红棕象甲的早期检测与管理。该方法融合计算机视觉、深度学习（DL）、物联网（IoT）及地理空间数据，有效检测和分类受红棕象甲侵染的棕榈树。主要阶段包括：（1）基于物联网设备声音数据的深度学习分类；（2）利用YOLOv8对无人机影像进行棕榈树检测；（3）基于地理空间数据绘制红棕象甲分布图。我们定制的深度学习模型在检测与定位受侵染棕榈树方面实现了100%的精确率和召回率。通过集成地理空间数据，可构建全面的红棕象甲分布图，从而支持高效监测与针对性管理策略。这种技术驱动的方法将帮助农业管理部门、种植者和研究人员管理红棕象甲侵染，保障棕榈树种植园的生产力。