Teses e Dissertações
URI permanente desta comunidadehttps://locus.ufv.br/handle/123456789/1
Teses e dissertações defendidas no contexto dos programas de pós graduação da Instituição.
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Resultados da Pesquisa
Item Sampling plans and application of neural networks to forecast the seasonal dynamics of Bemisia tabaci in soybean crops(Universidade Federal de Viçosa, 2023-07-08) Arcanjo, Lucas de Paulo; Picanço, Marcelo Coutinho; http://lattes.cnpq.br/2154671117191109Soybean Glycine max (L) (Merr) is the most produced, consumed, and traded legume worldwide. After its establishment in Brazil, Bemisia tabaci became a notorious sucking pest on soybean. Robust sampling plans and seasonal dynamic studies of B. tabaci in tropical soybean areas are essential to technicians and farmers early detect pest populations and plan sprays to manage this pest on time. The aim of this study is to determine a sampling plan and seasonal dynamics of B. tabaci in soybean crops through artificial neural networks. These studies were carried out in soybean commercial fields. Whitefly density, climatic elements and soybean age were assessed to support the dataset. In the seasonal dynamic studies, artificial neural networks were developed and selected to study this pest dynamic. The sampling design in this study is composed of 49 samples. The sampling unit and technique are the apical part of the soybean canopy and beating a plastic tray against plant apex, respectively, throughout the plant stages. The artificial neural network structure selected to determine the seasonal dynamic of B. tabaci in soybean crops has five entries (soybean age, average temperature, rainfall, wind speed, and atmosphere pressure) and four neurons in the hidden shell. This model previews whitefly adults with high accuracy from seven days of lag; it is reliable for modelling the seasonal dynamics of the whitefly B. tabaci in soybean crops. In conclusion, this study provides technical tools to scout, early detect, and plan sprays against the whitefly population, avoiding pest outbreaks. Keywords: Sucking pests. AI. Conventional sampling plan. Forecast.Item Decision-making systems for management of the invasive pest Neoleucinodes elegantalis (Guenée) (Lepidoptera: Crambidae) in commercial tomato crops according to insecticide spray method and plant stage(Universidade Federal de Viçosa, 2020-07-20) Arcanjo, Lucas de Paulo; Picanço, Marcelo Coutinho; http://lattes.cnpq.br/2154671117191109Neoleucinodes elegantalis (Guenée) (Lepidoptera: Crambidae) is a challenging pest to manage in tomato crops and can lead up to 90% of losses. Decision-making systems are an essential tool to manage N. elegantalis in integrated pest management (IPM) programs, which include economic injury levels (EIL) and sampling plans. In this study, we determined a decision-making system, based on sequential sampling plans for N. elegantalis eggs, according to insecticide spraying methods (hand sprayer, tractor, and aircraft) and plant stages [fruiting stage one (FSI) which has ≤3 clusters and fruiting stage two (FSII) which has >3 clusters]. Decision-making systems were determined under real conditions using data collected from 260 commercial fields. This study is the first to explore different decision-making systems for insect pests as a function of pesticide spray methods. EILs ranged from 0.105 to 0.239 fruits with eggs per sample depending on the insecticide spray method and plant stage. EILs were lower at the FSI for tractor, and aircraft sprays and higher for plants at FSII managed with hand sprayers. The sequential sampling plans led to correct decisions with time saving on the sampling process ranging from 37.76% to 65.40%. In conclusion, this decision- making system could be incorporated into N. elegantalis IPM programs on tomato crops because it makes correct, fast, and cost-effective decisions. Keywords: Sequential sampling plan. Small tomato borer. Economic injury level. Solanum lycopersicum.