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

• Published in: Insects (Basel, Switzerland) Vol. 12; no. 1; p. 47
• Main Authors: Barros, Pedro P S, Schutze, Inana X, Iost Filho, Fernando H, Yamamoto, Pedro T, Fiorio, Peterson R, Demattê, José A M
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 09-01-2021; MDPI
• Subjects: Agricultural practices; Agricultural production; Agriculture; Arthropods; Bemisia tabaci; Bioassays; Crop diseases; Crop management; Crop yield; Decision making; Discriminant analysis; Flowers & plants; glycine max; Infestation; Insecticides; Insects; Integrated pest management; Leaves; Monitoring; Multivariate analysis; Pest control; pest management; Pests; Photosynthesis; Population; Populations; Reflectance; sampling; Soybeans; spectroradiometer; Brazil; United States > US; glycine max; pest management; spectroradiometer; sampling
• ISSN: 2075-4450; 2075-4450
• DOI: 10.3390/insects12010047

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
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.