| Full text | |
| Author(s): |
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]
Total Authors: 6
|
| Affiliation: | [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
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | INSECTS; v. 12, n. 1 JAN 2021. |
| Web of Science Citations: | 1 |
| Abstract | |
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) | |
| FAPESP's process: | 17/19407-4 - Remote sensing techniques for monitoring arthropod pests in agricultural fields |
| Grantee: | Pedro Takao Yamamoto |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |
| FAPESP's process: | 19/26145-1 - Monitoring insect pests in soybean fields using remote sensing |
| Grantee: | Fernando Henrique Iost Filho |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 13/22435-9 - Hyperspectral data use for prediction of leaf nitrogen content in sugarcane |
| Grantee: | Peterson Ricardo Fiorio |
| Support Opportunities: | Program for Research on Bioenergy (BIOEN) - Regular Program Grants |