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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Monitoring Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) Infestation in Soybean by Proximal Sensing

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Autor(es):
Barros, Pedro P. S. [1] ; Schutze, Inana X. [2] ; Iost Filho, Fernando H. [2] ; Yamamoto, Pedro T. [2] ; Fiorio, Peterson R. [3] ; Dematte, Jose A. M. [4]
Número total de Autores: 6
Afiliação do(s) autor(es):
[1] Univ Fed Uberlandia, Civil Engn Coll, Monte Carmelo Campus, BR-38500000 Monte Carmelo, MG - Brazil
[2] Univ Sao Paulo, Dept Entomol & Acarol, BR-13418900 Piracicaba, SP - Brazil
[3] Univ Sao Paulo, Dept Biosyst Engn, BR-13418900 Piracicaba, SP - Brazil
[4] Univ Sao Paulo, Dept Soil Sci, BR-13418900 Piracicaba, SP - Brazil
Número total de Afiliações: 4
Tipo de documento: Artigo Científico
Fonte: INSECTS; v. 12, n. 1 JAN 2021.
Citações Web of Science: 1
Resumo

Simple Summary The whitefly Bemisia tabaci has become a primary pest in soybean fields in Brazil over the last decades, causing losses in the yield. Its reduced size and fast population growth make monitoring a challenge for growers. The use of hyperspectral proximal sensing (PS) is a tool that allows the identification of arthropod infested areas without contact with the plants. This optimizes the time spent on crop monitoring, which is important for large cultivation areas, such as soybean fields in Brazilian Cerrado. In this study, we investigated differences in the responses obtained from leaves of soybean plants, non-infested and infested with Bemisia tabaci in different levels, with the aim of its differentiation by using hyperspectral PS, which is based on the information from many contiguous wavelengths. Leaves were collected from soybean plants to obtain hyperspectral signatures in the laboratory. Hyperspectral curves of infested and non-infested leaves were differentiated with good accuracy by the responses of the bands related to photosynthesis and water content. These results can be helpful in improving the monitoring of Bemisia tabaci in the field, which is important in the decision-making of integrated pest management programs for this key pest. Although monitoring insect pest populations in the fields is essential in crop management, it is still a laborious and sometimes ineffective process. Imprecise decision-making in an integrated pest management program may lead to ineffective control in infested areas or the excessive use of insecticides. In addition, high infestation levels may diminish the photosynthetic activity of soybean, reducing their development and yield. Therefore, we proposed that levels of infested soybean areas could be identified and classified in a field using hyperspectral proximal sensing. Thus, the goals of this study were to investigate and discriminate the reflectance characteristics of soybean non-infested and infested with Bemisia tabaci using hyperspectral sensing data. Therefore, cages were placed over soybean plants in a commercial field and artificial whitefly infestations were created. Later, samples of infested and non-infested soybean leaves were collected and transported to the laboratory to obtain the hyperspectral curves. The results allowed us to discriminate the different levels of infestation and to separate healthy from whitefly infested soybean leaves based on their reflectance. In conclusion, these results show that hyperspectral sensing can potentially be used to monitor whitefly populations in soybean fields. (AU)

Processo FAPESP: 17/19407-4 - Técnicas de sensoriamento remoto para monitoramento de artrópodes-praga em agricultura
Beneficiário:Pedro Takao Yamamoto
Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE
Processo FAPESP: 19/26145-1 - Monitoramento de insetos pragas em soja utilizando sensoriamento remoto
Beneficiário:Fernando Henrique Iost Filho
Modalidade de apoio: Bolsas no Brasil - Doutorado
Processo FAPESP: 13/22435-9 - Utilização de dados hiperespectrais para predição do nitrogênio foliar em cana-de-açúcar
Beneficiário:Peterson Ricardo Fiorio
Modalidade de apoio: Auxílio à Pesquisa - Programa BIOEN - Regular