# A Novel Plug-in Board for Remote Insect Monitoring
## 1 Introduction  
In all fields of agriculture, a general goal is to increase the crop yield year by year. Since crop yields are affected by pests that may damage crops, farmers use insecticides periodically. However, a much more optimized approach would be to use insecticides only if the insect population exceeded a threshold level. Therefore, pest monitoring and density prediction in the field are important tasks for farmers. This forecast has economic (saving money) and environmental effects (less spraying, lower amounts of insecticides, etc.) because fruit growers can apply insecticides at the right time against insect pests [1]. To acquire quantitative information for pest density prediction, different types of traps can be used, such as light- [2], color- [3], and pheromone-based traps [4]. In this article, we mainly focus on sticky paper traps, where the trap has an attractive color and/or contains a pheromone. Conventional trap-based insect monitoring requires the manual counting and identification of pests. Manually counting pests in a trap is a slow and expensive process. In addition, the number of skilled persons who can distinguish insect species is limited. For such reasons, the automation of counting and identification tasks would be helpful. Furthermore, traditional pest counting does not give immediate feedback, while an automatized trap provides continuous sampling, and thus, pest population monitoring has a higher temporal resolution. The temporal resolution is important because if the number of pests cannot be obtained in time, it is impossible to take quick action. As an example, management decisions on the coding moth (Cydia pomonella), which is a harmful pest to apple fruit, rely on frequently checked trap captures [5]. For insect counting to be automatized, machine learning and embedded system technologies need to be merged. According to the state of the art of object detection, deep-learning-based detectors can be used with high efficiency not just in computer science but also in agriculture [6]. Since insect counting also relies on object detection, one or more stages of a CNN-based (Convolutional Neural Network) detector might be an obvious choice [7; 8]. Due to the success of deep object detectors, many researchers have focused only on image processing software, where sample images were taken by a high-resolution camera [9; 10]. However, in an automatized pest monitoring system, the sensing device is at least as important as the image processing software. Nowadays, IoT (Internet of Things) devices (with RGB cameras) are widely used for the remote monitoring of trap contents [2; 11]. In the market, commercial remote monitoring traps are already available, but growers are not satisfied with their scalability and cost. The authors of [5] pointed out that only a few growers apply commercial (smart) traps because they are expensive (approximately USD 1375/ha). In the past several years, some research groups also developed prototype smart traps for pest monitoring. [PERSON] et al. [12] designed a trap where the main controller unit is a Rapberry Pi 3 mini-computer, and the image sensing unit is a Raspberry Pi camera v2. Their trap requires WIFI coverage for wireless communication and an AC power socket to provide power consumption, which is between 400 and 600 mA, depending on the operating phase. Obviously, this type of trap can be used only in greenhouses where the necessary conditions are met. [PERSON] et al. [13] used Nvidia Jetson Nano and a Raspberry Pi camera to capture images of insects on sticky paper. Their prototype system can be used only indoors. [PERSON] et al. [14] designed a low-power camera board where the controller unit is a GAP8 SoC (System on Chip). The device consumes only 30 uW (microwatt) in deep sleep mode, but its power consumption in the active mode is unknown. The used grayscale camera has a rather low resolution (244 \(\times\) 324 pixels), and it is not easy to transpose deep object detector models to the device. [PERSON] et al.