Visión artificial para la detección de plagas en cultivos de tomate: revisión sistemática de la literatura

Jesús Alberto Alarcón Luna; María Salomé Alejandre Apolinar; Hugo Amores Pérez; Jacel Adame García; María Angélica Cerdán

doi:10.36825/RITI.14.34.010

Authors

Jesús Alberto Alarcón Luna TecNM/Instituto Tecnológico Superior de Xalapa https://orcid.org/0009-0001-5208-765X
María Salomé Alejandre Apolinar TecNM/Instituto Tecnológico Superior de Xalapa https://orcid.org/0009-0008-0232-8217
Hugo Amores Pérez TecNM/Instituto Tecnológico Superior de Xalapa https://orcid.org/0009-0009-0260-4872
Jacel Adame García TecNM/Instituto Tecnológico de Úrsulo Galván https://orcid.org/0000-0002-1982-1314
María Angélica Cerdán TecNM/Instituto Tecnológico Superior de Xalapa https://orcid.org/0000-0002-4250-8385

DOI:

https://doi.org/10.36825/RITI.14.34.010

Keywords:

Protected Agriculture, Pest Management, Horticulture, Agricultural Monitoring, Computer Vision

Abstract

Phytosanitary monitoring is an essential activity for reducing production losses caused by pests in tomato crops grown under protected agriculture conditions. In recent years, computer vision and deep learning methods have gained relevance as tools capable of automating agricultural image detection and analysis processes. Despite these advances, there is still a need to determine which techniques have been used, under what conditions they have been evaluated, and what performance they have reported, particularly in protected agriculture and small-scale production contexts. This study aims to examine research published on these technologies and assess their potential incorporation into participatory agricultural monitoring schemes. To this end, a systematic review of scientific literature published between 2020 and 2026 was conducted following the methodological guidelines proposed by Kitchenham. The review was developed through the stages of planning, study search and selection, and information synthesis, considering previously established inclusion and exclusion criteria. As a result, fifty studies related to image processing, artificial intelligence, and phytosanitary monitoring were selected and analyzed. Among the main findings of the review, several studies reported high-performance levels for deep learning models in tasks involving the automatic identification of symptoms associated with pests and other crop-related conditions. The synthesis identified the main techniques, data, application contexts, and reported performance levels, providing reference elements for assessing their potential incorporation into participatory agricultural monitoring schemes.

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Computer vision for pest detection in tomato crops: a systematic literature review

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