This paper presents a methodology for detecting the pest Aleurothrixus floccosus in citrus crops in Pedregal de Arequipa, Peru. The study employs simple random sampling during image collection to minimize bias, alternating and extracting leaves from different citrus trees. Image processing techniques, including noise reduction, edge smoothing, and segmentation, are applied for pest detection. Machine learning algorithms are used to classify the images, culminating in a robust detection methodology. A dataset of 1200 images was analyzed during the study.

The purpose of this article is to study a proposal for image processing and machine learning techniques to detect the pest Aleurothrixus floccosus in citrus in the area of Pedregal de Arequipa, Peru. Thus, in the collection of images a type of simple random sampling is adopted for the decrease of the bias in different citrus trees alternating and extracting leaves from the plantation also applying image processing techniques for noise reduction and edge smoothing, segmentation for the detection of the target and using machine learning algorithms that aims to perform the classification of the images, a methodology for the detection of the white fly or Aleurothrixus floccosus is achieved. It is necessary to understand that in the development of the article, it was possible to identify three stages of the pest in the plant, these being the presence of yellow areas, leaf rolling, and secretion of melase, likewise in the process it was possible to obtain a sample of 1200 images.