Índices de vegetação para predição da taxa de crescimento e colheita em alface

Abstract

Lettuce (Lactuca Sativa L.) culture has a short cycle. As a result, determining the ideal harvest point is challenging. Harvest point is determined visually, causing waste and important losses. Digital images and vegetation indices have been used to characterize quantitative and qualitative variables in diverse cultures; however, these indices have not been appropriately employed for lettuce. The objective of this study was to evaluate different vegetation indices to predict the growth rate and harvest point of lettuce. Twenty-five genotypes of biofortified green lettuce were evaluated. The vegetation indices GLI, NGRDI, SI, and HUE were calculated from images captured during plant development 1, 8, 18, 24, and 36 days after transplanting (DAT). The diameter and average leaf area of plants were measured using QGIS software. Green mass, plant diameter, stem diameter, and number of leaves were measured in the field. The means were compared using the Scott- Knott test (p ≤ 0.05) and simple linear regression models were generated to monitor the growth rate. Genetic dissimilarity was depicted using a dendrogram obtained by the UPGMA grouping method. Furthermore, validation between data collected in the field versus data obtained by imaging was performed using Pearson correlations. The evaluated genotypes showed genetic dissimilarity. Vegetation indices and the variables extracted through images were efficient for monitoring the growth rate and determining the harvest point for different lettuce genotypes. The agronomic variables measured in the field and the data extracted from the images had a high correlation. Overall, the vegetation indices SI, GLI, and NGRDI were efficient for monitoring the growth rate and determining the harvest point of different green lettuce genotypes.