ORIGINAL RESEARCH article
Front. Agron.
Sec. Pest Management
This article is part of the Research TopicModeling, Remote Sensing, and Machine Learning in Pest ManagementView all 4 articles
High-resolution suborbital remote sensing for detecting injuries caused by Saccharicoccus sacchari (Cockerell, 1895) (Hemiptera: Pseudococcidae) in sugarcane
Provisionally accepted
Allef Silva1Roberio Oliveira1Jacob Neto1Valeria Borges1Milton Guerreiro2Andre Amaral1Rhadija Souza1Antonio Nascimento1
José Bruno Malaquias3*- 1Federal University of Paraiba, Areia, Brazil
- 2UTEQ, Quevedo, Ecuador
- 3Federal University of Paraíba, João Pessoa, Brazil
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The pink mealybug (Saccharicoccus sacchari) is a significant pest of sugarcane, causing reductions in growth, sucrose content, and crop productivity. Its efficient management requires accurate field monitoring methods. This study evaluated the use of suborbital remote sensing with Remotely Piloted Aircraft (RPAs) in detecting injuries caused by this pest, using the vegetation indices NDVI and NDRE. The research was carried out in two periods (2023 and 2024), in an area of 1.5 hectares located in Alagoa Grande, Paraiba State, Brazil. conducted over two periods (2023 and 2024) in an area of 1.5 hectares located in Alagoa Grande, PB. Flights with a multispectral RPA and field surveys were conducted to determine the proportion of plants with low (<20 individuals/plant) and high infestation (>20 individuals/plant). Data were analyzed using both supervised and unsupervised machine learning methods, including principal component factor analysis. The results indicated a significant negative correlation between NDRE and low levels of infestation, while NDVI was more effective in detecting severe infestations. Multivariate analyses reinforced the complementarity of the indices in identifying the pest, with NDRE standing out in early detection and NDVI in identifying advanced damage. Kappa coefficients (> 0.80) comparing plant infestation with NRDE and NDVI yielded excellent results, with an overall Accuracy and Sensitivity of over 80.00% for the relationship between NDVI and high infestation per plant, and 73.33% and 83.33%, respectively, for NDRE with low infestation per plant. It is concluded that the integrated use of NDVI and NDRE, via remote sensing, is a promising tool for monitoring S. saccharipink mealybug in sugarcane, contributing to faster and more efficient decision-making in pest management.
Keywords: imaging, RPA, IPM, Sampling, Pink mealybug
Received: 17 May 2025; Accepted: 24 Oct 2025.
Copyright: © 2025 Silva, Oliveira, Neto, Borges, Guerreiro, Amaral, Souza, Nascimento and Malaquias. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: José Bruno Malaquias, malaquias.josebruno@gmail.com
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