Automatic Disease Detection in Physalis Peruviana Based on Image, a Review Systematic

In this study, we present a bibliographic compilation on the detection of plant diseases using computer vision, focusing on Physalis peruviana, known as Uvilla, Uchuva, Golden Berry, and Aguaymanto. A problem has been identified in the manual detection of diseases and fruit damage due to the unique characteristics of the plant, which has a protective covering that creates uncertainty about the condition of the fruit. The research gathered articles from journals indexed in Scopus, IEEE Xplorer, and Web of Science, selecting 57 documents, of which 49 provide detailed information on computer vision in disease detection and Physalis peruviana, representing 13.2% of the total. Various techniques and methods, as well as image processing algorithms that improve quality before analysis, were classified. The types of images used, along with the detection of pests and diseases affecting the plant, were also described. Additionally, the visible effects on leaves, fruits, and stems according to the caused disease were shown. An initial experiment in image processing was conducted to pave the way for a subsequent application of disease detection algorithms using computer vision.