This study presents the preliminary findings from a field experiment aimed at evaluating the health status of Capsicum pubescens crops, commonly known as rocoto in Peru, using unmanned aerial systems (UASs) equipped with multispectral sensors. C. pubescens is a neglected and underutilized crop with significant agroecological and nutritional value, traditionally cultivated in the Andean highlands but lacking modern cultivation and monitoring practices. The experiment was conducted in the province of Rodríguez de Mendoza, Amazonas, Peru. A DJI Mavic 3M drone, fitted with a multispectral camera, was used to collect aerial imagery over three rocoto crop plots during the production stage. From these images, a Green Area Index (GAI) map was generated, and ten vegetation indices (VIs) were calculated to assess the plant health. Key indicators included the Green Chlorophyll Index (CIgreen), which reached a maximum of 1.22, and the Red Edge Chlorophyll Index (CIred edge), with a peak value of 1.16, both suggestive of active vegetation with a moderate chlorophyll content. The Normalized Difference Vegetation Index (NDVI) and Green NDVI (GNDVI) reached values of 0.99 and 0.92, respectively, indicating a healthy physiological status and high productive potential. These vegetation indices will serve as input variables for machine learning models (multiple linear regression, support vector machines, and random forests), which will be used, combined with feature selection techniques, to further refine crop health assessments. The integration of UAS technologies and artificial intelligence represents an innovative approach to modernize agricultural monitoring of neglected crops such as C. pubescens. This effort supports progress toward the broader goal of revitalizing the cultivation of traditional crops in the Andean–Amazonian region, enhancing food security, and adapting agriculture to climate variability.