# A Low-Cost Remotely Configurable Electronic Trap for Insect Pest Dataset Generation
## 4 Discussion  
Digitalization can make a major contribution to one of the main challenges of IPM of pests of agricultural, forestry, urban, or medical importance by, namely, reducing the workload and increasing the decision-making accuracy. It is well known that different regions may be infested by different types of pests. Deep Learning and other AI techniques are based on learning objects using images with enough quality. However, learning objects that will not be likely to appear in that environment would be useless and could even provoke forgetting the actual required objects. To obtain the best classifier, a dataset containing images of the local pests should be used. In this sense, the proposed system will be able to provide specific datasets of local pests with enough quality images. This proposal aimed to design and implement an electronic trap system based on remote configurability for insect monitoring, therefore allowing remote reconfigurations in real time to adjust the acquisition parameters to the specific requirements of the object of study. Recent studies performed image acquisition manually using smartphones, pursuing the optimization of acquisition parameters with the aim of improving downstream stages, namely classification [15] or even using smartphones to achieve precise adjustments in the quality of image acquisition [20]. There is currently an open debate or dilemma in the scientific community between pest detection at the edge and external service processing [22; 25]. The former has garnered significant interest, as these systems directly transmit information about the number of insects (and potentially, the type of each) to a central server. These devices are intended for long-term use and entail higher computational load. Consequently, they are equipped with solar panels and batteries. However, both implementations have their advantages and limitations; edge processing, for instance, minimizes communication requirements, while external server processing provides greater computing capacity and facilitates the acquisition of new images for future training stages. Our study has focused on the image acquisition device, with processing on an external server, aiming to integrate remote reconfigurations for enhanced flexibility and adaptability to various pests and conditions. It is important to note that a key advance in this field is the ability to make changes remotely. Being able to remotely adjust the way the system  
Figure 14: Example of an image captured by the OV-2640 (RYS model) camera. captures images in real time is essential to adapt to changing survey conditions and different insect pest situations [39]. This feature makes the system more resilient and effective, representing a major step forward in insect monitoring technology [39]. Therefore, it is noteworthy that the innovation presented in this research lies in the remote reconfiguration capability enabled through intuitive web interfaces provided by the API (Swagger) and the WebServer, allowing for real-time dynamic adjustments. This capability eliminates the need for on-site visits and reduces the dependence on specialized knowledge for implementation and testing. Additionally, image quality is automatically validated using pre-defined objective metrics, ensuring system accuracy without direct expert intervention. Thus, thanks to these configuration interfaces and the automatic image quality validation system, the deployment and configuration can be managed by any user, regardless of their technical expertise level. This novel feature ensures the adaptability of the system to diverse insect pests and specific study conditions, thereby amplifying its efficacy in capturing accurate and pertinent data. Apart from this unique property of remote configuration, our device has been designed with a criterion that is also key to a trap, such as its cost-effectivity.