During the last two decades Red Palm Weevil (RPW, Rynchophorus Ferrugineus) has become one of the most dangerous threats to palm trees in many parts of the World. Its early detection is difficult, since palm trees do not show visual evidence of infection until it is too late for them to recover. For this reason the development of efficient early detection mechanisms is a critical element of RPW pest management systems. One of the early detection mechanisms proposed in the literature is based on acoustic monitoring, as the activity of RPW larvae inside the palm trunk is audible for human operators under acceptable environmental noise levels (rural areas, night periods, etc.). In this work we propose the design of an autonomous bioacoustic sensor that can be installed in every palm tree under study and is able to analyze the captured audio signal during large periods of time. The results of the audio analysis would be reported wirelessly to a control station, to be subsequently processed and conveniently stored. That control station is to be accessible via the Internet. It is programmed to send warning messages when predefined alarm thresholds are reached, thereby allowing supervisors to check on-line the status and evolution of the palm tree orchards. We have developed a bioacoustic sensor prototype and performed an extensive set of experiments to measure its detection capability, achieving average detection rates over 90%.

Monitoring pest insect populations is currently a key issue in agriculture and forestry protection. At the farm level, human operators typically must perform periodical surveys of the traps disseminated through the field. This is a labor-, time- and cost-consuming activity, in particular for large plantations or large forestry areas, so it would be of great advantage to have an affordable system capable of doing this task automatically in an accurate and a more efficient way. This paper proposes an autonomous monitoring system based on a low-cost image sensor that it is able to capture and send images of the trap contents to a remote control station with the periodicity demanded by the trapping application. Our autonomous monitoring system will be able to cover large areas with very low energy consumption. This issue would be the main key point in our study; since the operational live of the overall monitoring system should be extended to months of continuous operation without any kind of maintenance (i.e., battery replacement). The images delivered by image sensors would be time-stamped and processed in the control station to get the number of individuals found at each trap. All the information would be conveniently stored at the control station, and accessible via Internet by means of available network services at control station (WiFi, WiMax, 3G/4G, etc.).