Banca de QUALIFICAÇÃO: ALLEF DE SOUZA SILVA

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
DISCENTE: ALLEF DE SOUZA SILVA
DATA: 21/10/2026
HORA: 09:00
LOCAL: Laboratório de Entomologia
TÍTULO: Multispectral imaging to validate the effectiveness of the combination of biological and chemical management of Spodoptera frugiperda in a region with high selection pressure
PALAVRAS-CHAVES:
PÁGINAS: 10
RESUMO: The fall armyworm (Spodoptera frugiperda) represents one of the main phytosanitary challenges in corn cultivation, especially in regions with high selection pressure. This study aimed to evaluate the effectiveness of integrated management, combining chemical and biological control, and to verify the use of spectral indices (NDVI and NDRE) obtained via remote sensing with drones as tools for monitoring biotic stress. The experiment was conducted in an experimental area of Bt corn (NK 501 VIP3 and NK 509 VIP3), using a randomized block design with six treatments and four replications. The treatments included a control (no application), a conventional chemical control, and a chemical control with a spray mixture of Bacillus thuringiensis and Brevibacillus laterosporus. We carried out four sequential pesticide applications, varying according to the management adopted. Infestation by S. frugiperda was quantified visually using the Davis scale, which classifies leaf damage into nine levels. These data were correlated with multispectral images obtained by remotely piloted aircraft and analyzed with a machine learning model. The results indicated that both chemical control alone and its combination with biological control significantly reduced the proportion of plants with high levels of damage. The NK 509 VIP3 cultivar showed the most significant negative correlation between spectral indices and damage levels, highlighting the sensitivity of NDVI and NDRE in detecting physiological variations induced by herbivory. NDRE, in particular, showed greater discriminatory capacity at later crop stages, as shown by machine learning metrics such as Accuracy and sensitivity above 80.00%. The findings reinforce the importance of integrated management and precision agriculture technologies in sustainable pest control. The combination of integrated management strategies and drones equipped with multispectral sensors enables intelligent monitoring of the damage caused by S. frugiperda, promoting significant advances in digital and sustainable agriculture.
MEMBROS DA BANCA:
Presidente(a) - 1840849 - JOSE BRUNO MALAQUIAS
Interno(a) - 1092436 - FABIO MIELEZRSKI
Externo(a) à Instituição - JOSE EDNILSON MIRANDA
Externo(a) à Instituição - MIGUEL FERREIRA SORIA
Externo(a) à Instituição - SILVANA APARECIDA DE SOUZA