# A Low-Cost Remotely Configurable Electronic Trap for Insect Pest Dataset Generation
## 1 Introduction  
Integrated pest management (IPM) is currently considered the primary strategies for reducing pesticide usage [1, 2, 3]. Indeed, low pesticide-input IPM systems are of obligatory implementation in the European Union Member States (Directive 2009/128/EC) and the United States [4]. The Directive 2009/128/EC of the European parliament and of the council establishing a framework for Community action to achieve the sustainable use of pesticides defines IPM as a \"careful consideration of all available plant protection methods and subsequent integration of appropriate measures that discourage the development of populations of harmful organisms and keep the use of plant protection products and other forms of intervention to levels that are economically and ecologically justified and reduce or minimize risks to human health and the environment\". In this regard, the efficiency of the decision-making process based on economically and ecologically robust intervention levels relies on the accuracy of the techniques used for monitoring insect pests' population dynamics [1, 2]. Thus, the data collection procedure should be improved to ensure the best implementation of the subsequent agricultural operations. In the context of IPM, traps are considered effective tools for attracting insects, thus allowing the determination of the seasonal population to make decisions for control in case populations exceed treatment thresholds [2]. These traps are also allowing the study of insect behavior, such as flight, oviposition, parasitism, mating/calling, response tosemiochemicals, and others [5]. To improve trapping efficiency, lures were traditionally used as a main component of the trapping system to enhance their efficiency of specificity, with colors and odors being the primary monitoring tool complements [6]. Nevertheless, the conventional method of monitoring insect pests is highly time-consuming, very expensive, and lacks precision, primarily due to a deficiency in the expert knowledge required to differentiate relevant species [7]. To address this, the integration of technology is crucial to streamline the process, resulting in time and cost savings, and enhancing the efficiency and effectiveness of trapping systems. It is, therefore, essential to further research low-cost alternatives that provide effective and economical solutions. Significant progress is being made in the biomedical [8], industrial [9], and marine biology [10] sectors. In this regard, automated monitoring has been introduced as a new tool that plays a crucial role in IPM functionality. However, the practical application of these techniques is hindered by several issues, encompassing challenges in both structure and functionality. This hinders the ability to achieve precise localization and accurate identification of pest organisms across diverse cropping systems. Additionally, incorporating these systems into a wider market share of precision spraying machinery for horticultural production is crucial [11]. As of now, there is a limited presence of innovative solutions in the market, particularly those offering automated identification of insect pests. Some works have been carried out to develop pest detection and classification systems. According to [PERSON] and [PERSON] [12], there are three types of insect pest monitoring systems: fully automated, semiautomatic, and manual. The most common ones are the manual systems, in which manual tasks are used for each of the stages: deployment, gathering, detection, counting, and classification. These systems are highly time-consuming, tedious, and largely susceptible to errors. Obviously, these systems cannot deal with online real-time monitoring of the environment. To automatize the detection of pest individuals, computer imaging [13; 14] has gained large importance in this type of electronic traps. [PERSON] et al. [15] introduced a methodology for pest classification in viticulture. Images from traps were manually captured using smartphones by winegrowers and taxonomy specialists.