Advanced search
Start date

Monitoring insect pests in soybean fields using remote sensing

Grant number: 19/26145-1
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): February 01, 2020
Effective date (End): January 31, 2023
Field of knowledge:Agronomical Sciences - Agronomy - Plant Health
Acordo de Cooperação: Koppert Brasil
Principal Investigator:Pedro Takao Yamamoto
Grantee:Fernando Henrique Iost Filho
Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
Host Company:Universidade de São Paulo (USP). Escola Superior de Agricultura Luiz de Queiroz (ESALQ)
Associated research grant:18/02317-5 - Center for Excellence in Biological Control, AP.PCPE


One of the bases for implementing Integrated Pest Management (IPM) programs is the determination of pest population levels in the field, for later making decisions about controlling such pests, based on known action thresholds. However, pest control in Brazilian soybean fields has not been done based on IPM precepts, but instead, based on "calendar" spraying, which harms the ecosystem's balance. This happens mostly because of the particular characteristics of soybean production in Brazil, such as large fields, succession with crops that share the same pest species, large use of fungicides, previous sales of production and shorter crop cycles. Aiming at recovering the system's sustainability, this study proposes the use of Remote Sensing techniques to optimize pest sampling in soybean fields, providing subsidy for growers to adopt IPM practices, therefore using control methods only when and where pest populations reach action thresholds. To do so, semi-field studies will be carried, with different infestation levels of economically important pest species, being two defoliator caterpillars (Spodoptera eridania and Chrysodeixis includens) and two stink bugs (Dichelops melachantus and Euschistus heros). Infested plants will be monitored for their reflectance using a hyperspectral imaging sensor. Furthermore, field studies will be carried out, where the natural infestation of different species might happen, using a multispectral imaging sensor attached to aa unmanned aerial vehicle. Besides, field samplings will be done in loco, and infested plants will be taken to the lab for reflectance analysis, using the hyperespectral sensor. We expect to correlate the infestation levels observed in the field with multi and hyperspectral responses, to create a base of information to be used for sampling soybean pests in other regions of Brazil, by using remote sensing tools.

News published in Agência FAPESP Newsletter about the scholarship:
Articles published in other media outlets (0 total):
More itemsLess items

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
IOST FILHO, FERNANDO HENRIQUE; PAZINI, JULIANO DE BASTOS; DE MEDEIROS, ANDRE DANTAS; ROSALEN, DAVID LUCIANO; YAMAMOTO, PEDRO TAKAO. Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging. AGRONOMY-BASEL, v. 12, n. 7, p. 20-pg., . (19/26099-0, 18/02317-5, 17/19407-4, 19/26145-1)
BARROS, PEDRO P. S.; SCHUTZE, INANA X.; IOST FILHO, FERNANDO H.; YAMAMOTO, PEDRO T.; FIORIO, PETERSON R.; DEMATTE, JOSE A. M.. Monitoring Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) Infestation in Soybean by Proximal Sensing. INSECTS, v. 12, n. 1, . (17/19407-4, 19/26145-1, 13/22435-9)
BARROSO, GEOVANNY; GODOY, LUCAS LORENA; IOST FILHO, FERNANDO HENRIQUE; YAMADA, MARIANA; RABELO SANTANA, EMILE DAYARA; PAZINI, JULIANO DE BASTOS; DE QUEIROZ OLIVEIRA, LUANA VITORIA; YAMAMOTO, PEDRO TAKAO. Predator-Unfriendly Pesticides Harm the Beneficial Mite Neoseiulus idaeus Denmark & Muma (Acari: Phytoseiidae). AGRONOMY-BASEL, v. 13, n. 4, p. 17-pg., . (19/26145-1, 20/15134-6, 18/02317-5, 19/26099-0)
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
IOST FILHO, Fernando Henrique. Monitoring soybean pests using remote sensing. 2023. Doctoral Thesis - Universidade de São Paulo (USP). Escola Superior de Agricultura Luiz de Queiroz (ESALA/BC) Piracicaba.

Please report errors in scientific publications list using this form.