**1. Introduction**

Smart and precision agriculture are the most evolving trends in the modern agriculture industry. The electronic sensors can be embedded into the plants to obtain necessary data for aiding in decision-making to detect pest infestation or to improve crop quality. Concerning the date palm, there is an increasing demand to apply modern technology and smart agriculture for early detection of the RPW in the date palm plantations or other palms used for landscaping. RPW is the most dangerous pest for palms worldwide, which can cause irreversible damages, particularly at the late stage of infestation. Therefore, RPW, referred to as the palm cancer, is considered one of the most dangerous pests in the Mediterranean, Gulf Cooperation Council, part of Europe, and East Asia facing date and oil palm tree [1]. The most challenging problem in the control of RPW is the early detection of infestation,

which is difficult because symptoms caused by the weevil are only visible when an infestation is more advanced. The highly infested palms are often destroyed and removed [2]. For this purpose, there is a requirement to use advanced technology for the early detection of the RPW. Early detection is an effective solution to control and eradicate RPW successfully. So far, the RPW detection occurs depending on the visual inspection by laborers, which is entrusted by private or public organizations. The inspection by laborers is very costly for owners of private farms; it is likely not to be implemented due to the high cost [3]. Therefore, the frequent inspection has never been applied systematically and on a large scale, even when the inspections are compulsory [4]. Although, the early detection of the RPW presence could mean a significant economization in the capital, investment in the farms, and providing job opportunities for agricultural workers [2]. RPW early detection is a major challenge due to the cryptic nature of the weevil in most of its developmental stages. RPW larva is the most dangerous stage in the pest life due to the direct destruction it causes on the infested palms. Thus, most of the early detection methods concentrate on this stage of the RPW life cycle [2, 5], although the presence of RPW adults in the palm plantation is one of the most indicative proofs of the infestation. Governments have successfully and widely used pheromone traps as the primary protocol in the integrated management to control the RPW [6, 7]. For that, the aggregation pheromone traps of RPW were included in the integrated pest management (IPM) as an essential technique to control RPW. There were unique developments in the trap designs, color, and trap density, besides the improvement of trap catching by the addition of kairomones in various forms. However, limited human resources and high transportation costs reduced the rate of monitoring and mass trapping of RPW adults [3]. Recently, there are strong ongoing efforts to develop a reliable and quick system for early detection of RPW using a combination of computer science, sensors, and modern electronic technologies. The most important and promising technologies are X-ray [2, 8, 9], acoustic systems [5, 10–17], remote sensing systems [18–21], and radio telemetry [7]. To control the RPW, it is necessary to implement an innovative and practical early detection method leading to reduce the pest population as much as possible. This action is essential, as the early detection of infested date palm trees allows the owner of farms to sanitize or to eradicate them in the event of a severe infestation. The early detection followed immediately by the palm sanitization and eradication of infested palms' parts allows to limit or prevent the RPW spread to the neighboring plantations, thus eliminating the RPW as quickly as possible [4]. The physical properties of different developmental stages of the weevil, such as sound, thermal and chemical emissions, and images are used in the early detection technologies. The main objective of this chapter is to provide an overview and engineering information about the new trends in early detection of RPW and to discuss the basic principles of the most current and promising technologies for RPW detection.
