Banca de QUALIFICAÇÃO: BILTON GILBERTO NHANTUMBO

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
STUDENT : BILTON GILBERTO NHANTUMBO
DATE: 17/12/2024
TIME: 10:00
LOCAL: Laboratório de Modelagem Ambiental e Atmosférica - IF
TITLE:

DIGITAL PHENOTYPING APPLIED FOR EVALUATION OF QUALITY OF SEEDLINGS OF FOREST SPECIES

KEY WORDS:

Moringa oleifera, digital phenotyping, vegetative indices, neural networks.

PAGES: 15
BIG AREA: Ciências Agrárias
AREA: Agronomia
SUBÁREA: Agrometeorologia
SUMMARY:

The demand for high-quality seedlings has been growing due to the need for vigorous plants for reforestation and sustainable crops. This study aims to integrate digital phenotyping techniques and artificial neural network modeling to evaluate the growth of Moringa oleifera, a species recognized for its nutritional and medicinal value and adaptability to semiarid conditions. The experiment is being conducted in a completely randomized design (CRD), with 11 treatments based on different proportions of biosolids and Fertilurb, and 5 replicates per treatment, totaling 560 seedlings. Multispectral images will be captured to calculate vegetative indices (NDVI, GNDVI, SAVI, NDRE), correlating them with traditional measurements such as height, stem diameter and leaf area index. Neural networks will be implemented to model biometric variables from the vegetative indices, using exclusively data obtained by multispectral cameras. The study is being conducted at the experimental nursery of the Forestry Institute of UFRRJ. Ultimately, it is expected to develop non-invasive and efficient predictive models capable of monitoring seedling growth and proposing sustainable alternatives for large-scale production of Moringa oleifera, ensuring optimized management practices, guaranteeing greater productivity and seedling quality.

COMMITTEE MEMBERS:
Interno - 1574025 - GUSTAVO BASTOS LYRA
Interno - 1050615 - MURILO MACHADO DE BARROS
Externo ao Programa - 3040039 - MARCEL CARVALHO ABREU - UFRRJ