A inovação tecnológica é uma constante na agricultura de precisão e com ela vem uma necessidade de experimentação e confirmação por parte destas. Dentro desta panóplia de tecnologias, os dados fornecidos pelos sensores de deteção remota juntamente com outras informações, como por exemplo dados meteorológicos, permitem fornecer, ao empresário agrícola, uma alternativa aprimorada e muito mais viável em relação à observação in situ e assim tomar uma decisão mais consciente e sustentável. O Programa Espacial Europeu (ESA), entre outros, através do satélite Sentinel-2, fornece estes dados multiespectrais de forma gratuita, mas nem sempre são de fácil manipulação tornando o processo de análise moroso. Programas como o GranularLink, disponibilizam e manipulam dados/informação atempadamente, proporcionando ao empresário agrícola conhecimento que lhe permita tomar as melhores decisões. Esta dissertação teve como objetivo a comparação e avaliação de índices vegetativos, nomeadamente NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index) ou NDRE (Normalized Difference of Red Edge), calculados a partir de imagens obtidas diretamente dos programas de observação da terra versus plataformas. Este objetivo foi alcançado através da caraterização da evolução destes índices de vegetação na cultura do milho implementada na Parcela Grande (Pivot) da empresa Sociedade Agro-Florestal CampoDobrado, localizada em Vale de Figueira (Santarém) durante o ano de 2021. Foram encontradas diferenças ao nível da saturação, entre o NDVI e NDRE, em fases mais tardias da cultura e na correlação com o NDMI, em que o NDRE não satura tanto e obteve uma correlação superior. A variabilidade espacial, encontrada na parte direita do pivot, está de acordo com a forte relação da produtividade com os valores obtidos de NDVI e NDRE. A utilização da aplicação Granular Link, para o processamento e análise de imagens, evidenciou bastantes benefícios para o agricultor em relação ao método “tradicional” (via Sentinel-2) sendo a rapidez e facilidade de obtenção de imagens umas das principais vantagens. No entanto, caso seja necessária uma maior manipulação de dados, neste momento, apenas é possível através do download direto dessas imagens.

Os índices de vegetação são hoje amplamente utilizados para avaliar e caracterizar a cobertura vegetal. Na agricultura estes índices permitem obter informações ao longo do ciclo cultural e atuar oportunamente, caso se justifique. Neste estudo foram calculados diferentes índices de vegetação (NDVI - Normalized Difference Vegetation Index, NDRE - Normalized Difference Red Edge Index, EVI - Enhanced Vegetation Index) para avaliação do vigor vegetativo. Estes índices foram obtidos a partir de imagens do satélite Sentinel-2, da Agência Espacial Europeia, de maio a setembro de 2021. Esta análise incidiu sobre a cultura do milho, numa parcela situada no Ribatejo (38.994975°; -8.369681°). No processamento dos dados foram utilizados os softwares ArcGis TM e Microsoft Excel. Este trabalho tem como objetivos: a) desenvolver mapas temáticos de índice de vegetação para avaliar a produtividade da cultura do milho; b) analisar o comportamento dos índices de vegetação ao longo do ciclo cultural; c) perceber qual ou quais os índices que são mais confiáveis nas fases mais avançadas do ciclo cultural. A correlação entre o NDVI e o EVI foi forte (r=0,95) mas o EVI evidenciou diferenças na refletância do milho em fases do ciclo cultural em que o NDVI mostrou saturação. O NDRE apresentou uma forte correlação com o NDVI (r=0,99). Nas fases do ciclo cultural em que a cobertura do solo fica mais densa, principalmente na fase vegetativa da floração, o NDRE superou o EVI e o NDVI, mostrando ser eficiente na deteção espacial das diferenças do vigor vegetativo.

The advancement of technology associated with the field, especially the use of unmanned aerial vehicles (UAV) coupled with multispectral cameras, allows us to monitor the condition of crops in real time and contribute to the field of machine learning. The objective of this study was to estimate both productivity and above-ground biomass (AGB) for the corn crop by applying different vegetation indices (VIs) via high-resolution aerial imagery. Among the indices tested, strong correlations were obtained between productivity and the normalized difference vegetation index (NDVI) with a significance level of p < 0.05 (0.719), as well as for the normalized difference red edge (NDRE), or green normalized difference vegetation index (GNDVI) with crop productivity (p < 0.01), respectively 0.809 and 0.859. The AGB results align with those obtained previously; GNDVI and NDRE showed high correlations, but now with a significance level of p < 0.05 (0.758 and 0.695). Both GNDVI and NDRE indices showed coefficients of determination for productivity and AGB estimation with 0.738 and 0.654, and 0.701 and 0.632, respectively. The use of the GNDVI and NDRE indices shows excellent results for estimating productivity as well as AGB for the corn crop, both at the spatial and numerical levels. The possibility of predicting crop productivity is an essential tool for producers, since it allows them to make timely decisions to correct any deficit present in their agricultural plots, and further contributes to AI integration for drone digital optimization.

ABSTRACT This study aimed to evaluate the effect of different managements with pig slurry (PS) and cattle slurry (CS) on wheat dry matter and grain yields, and the potential to estimate this variation with the NDVI (normalized difference vegetation index) and NDRE (normalized difference red edge index) vegetation indices. The experiment was conducted in Frederico Westphalen-RS, Brazil, in a randomized block experimental design, in a 2 x 7 factorial scheme with four replicates and seven forms of surface and subsurface application, with and without the use of the dicyandiamide (DCD) nitrification inhibitor, distributed as follows: control without application of slurry and DCD; control without application of slurry and DCD, but scarified only with the injection machine; surface application; surface application with DCD; subsurface injection; subsurface injection with DCD; and application of NPK in mineral form. At flowering of the wheat crop, dry matter was collected and readings of NDVI and NDRE were taken, and finally grain yield was quantified. For PS and CS, best results of dry matter and grain yields were found in managements with subsurface injection and subsurface injection with DCD, respectively. Among the vegetation indices, only NDVI was efficient to estimate variations in the management using CS for wheat dry matter and grain yields.

ABSTRACT Geometric and radiometric corrections of images obtained by remotely piloted aircraft systems (RPAS) are required for the measurement of the spectral reflectance and calculation of vegetation indices. This study aimed to determine which radiometric calibration procedures allow obtaining suborbital images with more accurate reflectance data and spectral indices based on data collected by a Sequoia multispectral camera coupled to a RPAS on black oat cultivations at the flowering stage located in Eldorado do Sul, RS, Brazil. The orthophoto mosaics were processed without and with radiometric corrections by reflectance calibration ground target and/or solar irradiance sensor of the camera, and the radiometric accuracy of these images was evaluated using spectral reflectance ground data collected by the Crop Circle canopy sensor. The results indicated that the use of a reflectance calibration ground target is recommended to increase the radiometric accuracy of photographs even if the Sequoia solar irradiance sensor is employed. The use of the light gray target in the radiometric calibration resulted in orthophotos with the mean square error of reflectance values lower than 0.01 for all the analyzed bands and spectral indices.

Precision agriculture is a set of techniques that assist the monitoring of the agronomic performance of the maize crop by using vegetation indices. This study aimed to verify the relationship between vegetation indices, plant height, leaf N content, and grain yield of three maize varieties, grown under high and low N as topdressing. The experiment was carried out at the Fundação de Apoio à Pesquisa Agropecuária de Chapadão (Fundação Chapadão), located in the municipality of Chapadão do Sul, during the 2017/2018 season. The experiment consisted of a randomized block design with four replications, arranged in a 3x2 split-plot scheme. The first factor (plots) corresponded to three open-pollinated maize varieties (BRS 4103, BRS Gorotuba, and SCS 154), and the second factor (subplots) consisted of two N rates applied as topdressing (80 and 160 kg- 1). All the evaluated variables showed varieties x N interaction. Vegetation indices in maize varieties were influenced by the N rate applied as topdressing. Normalized Difference Vegetation Index (NDVI) and Soil-adjusted Vegetation Index (SAVI) showed a higher correlation with plant height. At the same time, Normalized Difference Red Edge (NDRE) had a stronger association with leaf N content.

Nas medidas de reflectância espectral da cobertura e uso do solo e para o cálculo de índices de vegetação são necessárias correções geométricas e radiométricas de imagens obtidas por sensores remotos acoplados a satélites e até mesmo a Veículos Aéreos Não Tripulados (VANTs). O objetivo desta dissertação foi avaliar a acurácia de dados de reflectância espectral e de Índices de Vegetação de Diferença Normalizada (NDVI) e Red Edge (NDRE) derivados de imagens obtidas por câmera Parrot Sequoia acoplada a um quadricóptero DJI Matrice 100, e de imagens do sensor MSI-Sentinel-2, em duas lavouras de aveia preta (Avena strigosa Schrec) na fase 60 do estádio fenológico, localizadas no município de Eldorado do Sul (RS - Brasil). Foram processados ortofotomosaicos (resolução espacial de 9,4 cm) sem e com correções radiométricas (com uso de painel de calibração de reflectância Mapir V2 e/ou com uso do sensor de irradiância solar do sensor Sequoia), e a acurácia radiométrica destas imagens foi avaliada a partir de dados de reflectância (178 amostras) coletados por um sensor Crop Circle ACS-430, no mesmo dia do levantamento com VANT (04/10/2018). O ortofotomosaico multiespectral com maior acurácia radiométrica foi o corrigido a partir do subpainel Light Gray. O Erro Absoluto Médio (EAM) das bandas e índices espectrais desse ortofotomosaico foi o seguinte: banda Red = 0,02 ±0,01; Red Edge = 0,3 ±0,2; NIR = 0,04 ±0,03; NDVI = 0,09 ±0,05; NDRE = 0,02 ±0,02. Este ortofotomosaico foi usado para avaliar a acurácia radiométrica de uma imagem (184 pixels) do sensor MSI-Sentinel 2 (05/10/2018), sem correção atmosférica (dados ao topo da atmosfera - TOA) e com correção atmosférica (dados de reflectância superficial - BOA) pelos métodos Dark Object Subtraction (DOS) e Sen2Cor. Os maiores valores de EAM ocorreram na imagem da banda Red (máximo de 0,25 ±0,01) com relação às outras bandas e índices espectrais analisados. Esse erro poderia ser explicado pela sensibilidade ao pigmento clorofila B em 642 nm, que pode ser detectado somente na banda Red do sensor Sequoia e não pelo sensor MSI. O valor máximo de EAM da imagem Red Edge (0,08±0,01) e NIR (0,10±0,01) foram obtidos com a imagem Sen2Cor. Os valores de NDVI mais acurados (EAM = 0,02±0,01) foram os da imagem de reflectância TOA , ao passo para os valores de NDRE foram os da imagem Sen2Cor (EAM = 0,03±0,01). Esta última é um produto nível 2A fornecido pela Agência Espacial Européia (ESA) e neste experimento com aveia preta a imagem NDVI apresentou EAM máximo de 0,08±0,02.

Corn has great relevance worldwide due to its economic and social importance. Its production grows every harvest, and determining traits directly related to the grain yield is essential for crop management. The hypothesis of this study is that structural equation modelling (SEM) and factor analysis allow us to identify agronomic variables and vegetation indices (VIs) that most contribute to grain yield. Thus, the objective was to identify which agronomic traits and VIs are most associated with grain yield in corn using SEM and factor analysis. Field experiments were carried out during the 2017/2018 and 2018/2019 crop seasons. The experimental design was in randomized blocks with three replicates in a factorial scheme. The following agronomic traits were evaluated: ear length (EL), number of rows per ear (NRE), number of grains per row (NGR), plant height, ear insertion height, stem diameter, and grain yield. The VIs evaluated were: Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge (NDRE), Green Normalized Difference Vegetation Index, Soil-Adjusted Vegetation Index (SAVI). Data were subjected to factor analysis and structural equation modelling. The EL, NGR, and NRE were the agronomic traits that most related to corn yield. The VIs most related to yield were NDVI, NDRE, and SAVI. By identifying which agronomic variables and VIs are the most related to grain yield in corn, this study contributes to the decision-making process on HTP in corn and consequently to a higher efficiency of corn breeding programs aiming at selecting high-yielding genotypes.

Nowadays, there is a necessity for the use of tools in order to estimate yield potential during the development of corn crop. Thus, the aid of active optical sensors, or embedded in RPAS, in the generation of vegetation indices can provide significant information to the knowledge of the behavior and temporal relationship of these indices with yield parameters of agricultural crops. Normally, during the monitoring of the entire cycle of a crop, a large amount of data is generated, which is difficult to analyze and interpret because the relationships between variables are complex. The goal of this work was to use multivariate analysis techniques to better understand the relationship of corn yield under different nitrogen rates, using vegetation indices obtained from data from two distinct remote sensing platforms, one proximal and the other, the remotely piloted aircraft (RPA) at different phenological stages. The experiment was carried out by Carvalho (2019) in a crop area of the Federal University of Santa Maria with the corn cultivar. A randomized block design with five blocks and five treatments with nitrogen dose variations (N) after the culture emergence was used. Vegetation sensing was performed by two distinct platforms, sequoia sensor embedded in an RPA and Optrx sensor embedded in a bicycle, obtaining the NDVI, GNDVI, EVI2 and NDRE Vegetation Indices with the sequoia sensor embedded in the RPA, and NDVI and NDRE with the sequoia sensor embedded in the bicycle in the vegetative stages V5, V7, V9, V11 and V12. In addition to these variables, the number of plants, N levels in the plant, and yield data were obtained. In this work we used data from four blocks, containing 800 plots, but only data from 451 plots were used, due to the discarding of 349 plots, which did not vary from 14 to 18 plants at harvest and/or did not present readings for some variable, i.e., null value. The normality test was performed and the data did not present normality, the data was standardized in the software (Statistica 12). Data analysis was performed using multivariate statistics using Hierarchical Cluster Analysis (HCA), Factor Analysis (FA) and Principal Component Analysis (PCA) methods. It was concluded that two distinct groups were formed (Sequoia / ARP and Optrx), and there was a greater relationship between productivity data with IVs (NDRE, NDVI, GNDVI and EVI2) at phenological stage V9 and IV NDRE at phenological stage V12.