## 1 Introduction  
Integrated pest management relies on the accuracy of pest population monitoring techniques. Without gathering information about the population dynamics together with the related ecological factors it is almost impossible to execute the appropriate pest control at the right time in the right place [1,2]. Ecological factors in the environment can be classified as physical (e.g., temperature and humidity), chemical (e.g., chemical composition of the soil), and biological factors (e.g., pathogens and pests). Among these factors, pests are those that directly damage the crop, and pest control has always been considered the most difficult challenge to overcome. A well-known technique to perform pest control monitoring is based on the use of insect traps conveniently spread over the specified control area. Depending on the kind of insect, each trap is properly installed with pheromones or other chemical substances that attract the insect we want to capture. The traps are designed in such a way that insects entering in the trap are unable to leave it, so pest monitoring systems will periodically collect the data of each trap (captured individuals count) to perform an efficient pest control monitoring. The popular method to collect trap data consists on repeated field surveys where visual observation of traps is performed by a human operator to record the number of captured insects. The periodicity between two consecutive surveys is usually between 15 to 30 days. This method has two main drawbacks: (1) it is labor intensive and therefore costly and (2) all monitoring traps cannot be synchronized to measure the target pest population. Given that the traditional monitoring techniques are labor intensive and offer poor temporal resolution measurement, the dynamics of pest population density in the field cannot be accurately monitored. Consequently, a proper estimation for a target pest population will be limited to a long-term scale. These are the main reasons that justify the use of image sensor network technologies to perform automatic pest monitoring. Therefore, we will propose in this paper a low-cost system based on battery-powered wireless image sensors, that accurately monitor pest populations with a higher temporal resolution and a significant reduction of pest monitoring costs, as no human intervention is required during the monitoring process. There are a lot of wireless image sensor proposals in the literature that may be used in our target application, so we will provide a representative view of the available wireless image sensors platforms, in particular of the Imote2 [3], developed by the Intel Corporation and being manufactured by MEMSIC. It is an advanced platform especially designed for sensor network applications requiring high CPU/DSP and wireless link performance and reliability. The Imote2 contains an Intel XScale processor, PXA271, with an IEEE 802.15.4 radio (TI CC2420) and one onboard antenna. It has 256 KB SRAM, 32 MB Flash, and 32 MB SDRAM, and provides basic and advanced expansion connectors supporting: 3 xUART, I2C, 2 xSPI, SDIO, I2S, AC97, USB host, Camera I/F, GPIO. A mini USB port is also available to establish connection with a PC. The size of an Imote2 is 48 mm \(\times\) 36 mm. The CMUCam3 [4] is an ARM7 TDMI based fully programmable embedded computer sensor. It is equipped with a Philips LPC2106 processor that is connected to an Omnivision CMOS camera sensor module. Features of CMUCam3 include: CIF resolution (352 \(\times\) 288 pixels) RGB color sensor, open source development environment for Windows and Linux, and an MMC flash slot with FAT16 driver support able to store captured video into memory at 26 frames/s. It contains LUA (a lightweight scripting programming language) for rapid prototyping, software-based JPEG compression, basic image manipulation library, CMUCam3 image emulation, compatible connector with wireless motes (Tmote Sky, FireFly, 802.15.4), and FIFO image buffer for multiple pass hi-resolution image processing. It supports 64 KB RAM and 128 KB of ROM.