## 1 Introduction
The authors show results using only one trap, but there are two main limitations to this idea: (1) image sensor cost related to the own terminal and the cost of each image upload through 3G data service, and (2) the power consumption demanded by the smartphone which is solved by authors with the use of solar panels, although there is no further details about it. In [11] the authors propose a remote pest monitoring system based on traps equipped with image sensors that wirelessly reports traps pest status inside a greenhouse. This work is the closest one to our proposal. It is based on low-cost image sensor nodes and wireless communication support to deliver the captured images of trap contents to a central station, where image processing determines the number of captured individuals. It is restricted to a greenhouse area for detecting the number of trapped insects but without identifying them. Also, the power consumption is not optimized being the image sensor operational live limited to 34 h, as authors state in their experimental study. Although this would not be a serious problem in a greenhouse (limited area) where daily maintenance can be performed, in other applications like the ones we are interested on (tens of traps distributed over large areas) would be a great constraint due to the high maintenance costs derived from battery replacement. This question shows the importance of optimizing power consumption in the image sensor design. Consequently, we propose a pest monitoring system that, like similar works in the literature, will provide several benefits over the traditional manpowered trap monitoring process: (1) it works in an unattended mode, (2) it is able to significantly reduce the monitoring costs, (3) the temporal resolution of trap monitoring data is higher and may be programmable, and (4) trap monitoring data may be available in real time through an Internet connection. Our proposal is able to perform an efficient pest monitoring process based on the design of appropriate wireless image sensors distributed over the monitoring area. The main advantages with respect its competitors (described above) are: (1) higher scalability, being able to deploy in small monitoring areas (greenhouses) as in large plantation extensions, (2) low-cost wireless image sensor (less than 100 \(\upcels\) per sensor), (3) low-power consumption that allows zero maintenance during the operational life of nodes. Although our proposal could be used for monitoring several kinds of pests, our work is mainly focused on the Red Palm Weevil (RPW, _Rhynchophorus ferrugineus_ (Olivier)) pest that actually it is present in a large number of regions around the world attacking different species of palm trees (_i.e._, date palm, coconut palm, and royal palm). Therefore, our pest monitoring proposal is based on RPW traps equipped with wireless image sensors that are scattered over the monitoring area forming a Wireless Image Sensor Network (WISN). In Figure 1, we show an example of WISN deployment. Each trap, represented by a green circle, is equipped with a wireless image sensor that periodically takes a picture of trap contents that it is delivered to the control station. In Figure 1, control station is represented by a red triangle. Controlstation deals with (1) the storage of the captured images delivered by the image sensors, and (2) the processing of the captured images in order to determine the number of individuals found at each trap by means of image recognition algorithms like the one proposed in [12], that it is able to identify RPW individuals with a success rate higher that 95%. All the captured images with the corresponding metadata (timestamp, GPS location, processing results, _etc._) are conveniently stored in the control station. Also, this information is accessible in real time by means of internet service providers available at the control station location (WiFi, WiMAX, 3G/4G, ADSL, Cable, Satellite, _etc._).