# A Low-Cost Remotely Configurable Electronic Trap for Insect Pest Dataset Generation
## 1 Introduction
The collection of traps and image capture were performed manually. Multiple image processing techniques were then applied to enhance the image quality for subsequent detection and identification stages. The detection and identification of pests are carried out using lightweight Machine Learning (ML) models that can be directly executed on smartphones. Further studies, carried out by [PERSON] et al. [16], improved the detection of multiple pests and diseases with images taken from complex real environments. These authors proposed the use of an improved faster region Convolutional Neural Network (CNN), which provides better accuracy levels and faster detection rates than previous approaches. However, these authors do not deal with the procedure to automate the capture the images in the environment and use a High-Performance Computer to compute the detection task. In this context, [PERSON] et al. [17] indicated that CNN-based methods for pest localization and recognition are not sufficiently effective for practical field prevention. This is due to the varied scales and orientations of the pests. To address these challenges, they propose an effective data augmentation strategy involving rotating the images at various angles and then cropping them into different grids. Conversely, the study by [PERSON] et al. [18], driven by the need for extensive data and the considerable effort required for training deep neural networks, proposed a method to create a dataset consisting of synthetic insect images. This dataset is designed to provide meticulously labeled images, thereby facilitating insect detection tasks. Therefore, given the need for retraining and the diversity in scales and orientations of pests, there is a growing trend towards classification in cloud systems, which can adapt and retrain in real-time according to specific pest requirements. Additionally, these systemscan integrate with new devices developed using standardized interfaces such as API or MQTT. An example of such a system would be XFARM's IoT Hub [19]. Alternatively, the manual acquisition of images from traps requires specific parameters to be met for the obtained frames to be used in subsequent stages for pest detection and classification. [PERSON] et al. [20] outlined a two-step methodology for optimal image acquisition of vineyard insect traps. Parameters such as camera focus, shadow/reflection detection, automatic cropping of traps, and perspective correction are automatically adjusted on the smartphone used for image capture. Other authors emphasize the importance of the quality of pest identification and classification. For example, [PERSON] et al. [21] assessed the performance of an improved classifier based on a Convolutional Neural Long Short-Term Memory (CNLSTM). However, the proposal does not consider the image capture subsystem, which has been shown to significantly influence the quality of the response. The integration of electronics, computing, and wireless communications for the development of advanced and efficient devices and protocols has significantly impacted the evolution of fully automated insect monitoring systems. Certain studies [22; 23; 24; 25; 26; 27] represent a significant progress in the development of fully automated insect pest monitoring systems. [PERSON] et al. [23] conducted an extensive analysis of the components that these systems typically incorporate. Typically, fully automated insect pest monitoring systems are deployed in agronomic environments with a pre-set configuration, to monitor insect traps without human intervention (or with minimal maintenance). As part of their functionality, they make use of remote servers to send either the captured images or the result of their processing at the edge (usually the number of individuals captured). Among the challenges faced by this approach, the correct pre-set configuration is very relevant, because its optimization is based on several criteria.