Índices de vegetação para diagnóstico nutricional de nitrogênio na cultura do milho a patir de imagens geradas por drone

Abstract

In Brazil, due to the edaphoclimatic characteristics, the corn crop has a high productive potential, provided that appropriate soil correction and fertilization practices are adopted. In this sense, nitrogen fertilization deserves special attention, mainly because the most used fertilizer in Brazil is urea, whose dynamics in the soil is complex, and losses due to volatilization may occur. To avoid losses in production, the best strategy is to monitor the crop through foliar analysis which, despite costing production, does not provide an immediate result, which often does not allow timely intervention. In the present work, the objective was to diagnose nutritional N deficiency in maize through Vegetation Indices (NDVI and VARI) generated from images generated by drones. A randomized block design with four treatments and four replications was used. The treatments consisted of different doses to simulate nitrogen (N) deficiency, being (50, 100, 150, 200 and 250 kg of N ha-1). The N doses were divided into three phases, planting, V4 and V6 stages, respectively. Collections were carried out for foliar analysis of N levels, in three different phenological stages of the crop, V4, V8 and R2. Before each collection, flights were performed with a UAV equipped with a high-resolution multispectral camera, which allows the generation of images and subsequently the Vegetation Index (VI). The flights were carried out at noon, as the angles of the sun's rays are more vertical. The images were processed using the QGIS® software, following the equations of each index. After corn harvesting, the variables ear diameter (DE), number of rows of grains per ear (NGE), number of grains per row (NGF), thousand-grain weight (PMG), length of ears ( CME) and productivity (PROD). It was concluded that the studied indices (NDVI and VARI) can be used to optimize processes and increase the accuracy and nutritional monitoring of the crop, as they proved to be effective in detecting the nutritional status of the crop during all phases in which it was carried out. the evaluations, V4, V8 and R2. In relation to the main components of income, a positive correlation was found, where the NDVI index was more efficient than the VARI for predicting the components of income.