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Sensoriamento remoto para monitoramento de mosca branca, Bemisia tabaci biótipo B (Hemiptera: Aleyrodidae) em soja

I. Filho,Fernando Henrique

Published 2019 in Unknown venue

ABSTRACT

Remote sensing for monitoring whitefly, Bemisia tabaci biotype B (Hemiptera: Aleyrodidae) in soybean Pest outbreaks in commercial fields are unpredictable in relation to location and timing. However, an efficient Integrated Pest Management depends on the knowledge of the distribution of the insects as early as possible, before the population is established and reaches the threshold of Economic Injury. It is possible, however, to identify factors in the field that may make plants more attractive to insects, such as water stress. One possible way to try to predict pest outbreaks is to diagnose the susceptibility of plants to insects. Thus, the objective of this study is to describe the reflectance patterns of soybean plants stressed by either water stress and Bemisia tabaci biotype B infestation. Soybean plants were grown in greenhouse under different irrigation regimes (30, 50, 70 and 100% daily water recharge), and offered to B. tabaci Biotype B adults in both choice and non-choice bioassays. All the plants used in the bioassays were previously evaluated for their reflectance, using the FieldSpec® 3 hyperspectral sensor, to classify them later in the irrigation groups. After the susceptibility bioassays, new studies were carried out to evaluate the feasibility of using the hyperspectral sensor to classify plants under infestation and water stress, in a factorial scheme. Irrigation regimes were 70 and 100% daily water refill, and tests were performed with controlled and uncontrolled infestation. In the susceptibility tests, it was possible to observe that, when given the options, B. tabaci Biotype B adults lay more eggs in plants grown with 70 and 50% daily water refill. When adults have no options, no significant difference was observed between the amount of eggs deposited in all irrigation regimes. Regarding the classification of plants in groups, it is possible to state that the FieldSpec® 3 hyperspectral sensor provides sufficient information for this. In the controlled infestation trial, four distinct groups were generated, 70% water refill + infestation, 100% water refill + infestation, 70% water refill without infestation and 100% water refill without infestation. Discriminant analysis showed that, after the assay, the groups were statistically different. In addition, using a cross-validation, it was possible to classify the groups with 73.81% accuracy. In the test with uncontrolled infestation, three groups were generated, according to the level of infestation: low, medium and high. Likewise, the Discriminant analysis showed that there is a significant difference between the groups, and cross validation indicated that it is possible to classify the level of infestation with 91.98% accuracy. Therefore, it is possible to conclude that hyperspectral remote sensing may be an additive tool for Integrated Pest Management, both to evaluate the susceptibility of plants to pests and to identify healthy and infested plants.

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