Incorporating Machine Learning Techniques to Enhance Rodent Surveillance in Marginalized Urban Communities.

Effective management of rodent pests necessitates efficient population surveillance. Many of the available methods currently used for estimating rodent populations are either costly or time-intensive. Rodent trapping demands significant resources, while tracking plates (TP) require high technical expertise and weeks to months of dedicated effort to satisfactorily interpret the plates. Here, we propose integrating Machine Learning techniques to evaluate plates with signs of rodent marks and compare their accuracy with that of conventional human-interpreted plates. We employed the Otsu method to transform plates from RGB color images to grayscale images, highlighting regions of interest. Subsequently, we applied a global threshold to create binary images, assigning values above a globally determined threshold as 1s and others as 0s. The original images were transformed into new versions with 25 small samples, highlighting regions of interest based on the binary images. We used dimensionality reduction methods to identify the fundamental structure of high-dimensional data and determined the most important patterns of interest on the plates. Among the methods, Principal Component Analysis, Independent Component Analysis, and Legendre Moments methods were used to visualize patterns and conduct exploratory data analysis. The