Detection of the Pest Spodoptera frugiperda in Corn Cultivation Using Smart Traps and Computer Vision

• Wendell dos S. Silva UFC
• Bianca Soares UFC
• Valentine de L. Almeida UFC
• Leonardo Viana UFC
• Patrik L. Pastori UFGD
• Deborah M. V. Magalhães Unilab
• Atslands R. da Rocha UFC

DOI: https://doi.org/10.5753/wcama.2024.2376

Abstract

Crop health is a crucial concern in agriculture, which has led to the development of various technological approaches to ensure crop vitality. One of the challenges facing farmers is the need to combat pests, such as Spodoptera frugiperda, which significantly affects several types of crops, such as corn and cotton, on a global scale. Accurate monitoring of insect population density per unit area is essential for Integrated Pest Management (MIP) to provide farmers with essential information about the occurrence of pest species in their crops. However, this monitoring process is predominantly manual and requires producers. This article aims to present the development of a trap and a machine learning model for automatically detecting this pest in the field, thereby supporting decision-making for implementing MIP programs.

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