Fenotipagem de alto desempenho para monitorar a taxa de crescimento e determinar o ponto de colheita em alface vermelha

• Ano de defesa: 2022
• Autor(a) principal: Espíndola, Gustavo Mendes
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Agricultura e Informações Geoespaciais
• Palavras-chave em Português: Lactuca sativa L; hortaliças; imagens digitais; índices de vegetação; vegetables; digital images; vegetation indices; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA; Agronomia; Hortaliças; Processamento de imagens - Técnicas digitais; Alface - Imagens digitais
• Link de acesso: https://repositorio.ufu.br/handle/123456789/34721; http://doi.org/10.14393/ufu.di.2022.186

Lettuce (Lactuca sativa L.) is a crop of great socioeconomic importance. It has a very short lifecycle; hence, following its growth is challenging. Brazil has several commercial plantations to supply large fast-food chains. These producers monitor the growth rate daily, in person, after transplantation. Owing to the fast growth of the plant and the large sizes of the plantations, the producers face significant food losses and waste. Therefore, the objective of this study was to monitor the growth and determine the harvest point of red lettuce based on different vegetation indices. Images obtained using an unmanned aerial vehicle were used to calculate the diameter of the plant, leaf area, and vegetation indices (BI, GLI, HUE, SCI, and SI). Agronomic parameters (plant diameter, stem diameter, green mass, number of leaves, and leaf temperature) were also measured in the field to validate the effectiveness of the different vegetation indices. Measurements were performed on 30 red lettuce genotypes. The means of the parameters were compared using the Scott-Knott test (p ≤ 0.05). The genetic dissimilarity was represented using a dendrogram obtained by the UPGMA hierarchical method. Pearson’s correlation was used to validate the consistency between the data obtained from images. The evaluated genotypes presented high genetic variability. The vegetation indices (BI, GLI, HUE, SCI, and SI) highly correlated with the plant diameter and leaf area obtained from the images. High-throughput phenotyping could be used to monitor the growth and determine the harvest point of different red lettuce genotypes, and is hence an excellent tool for producers.