Imagens multiespectrais para avaliação de índice de área foliar e massa seca do capim 'Tifton 85', sob adubação nitrogenada.Ciência Rural, v.45, n.4, abr, 2015. 697Imagens multiespectrais para avaliação de índice de área foliar e massa seca do capim 'Tifton 85', sob adubação nitrogenada

This study is aimed at (i) estimating the angular leaf spot (ALS) disease severity in common beans crops in Brazil, caused by the fungus Pseudocercospora griseola, employing leaf and canopy spectral reflectance data, (ii) evaluating the informative spectral regions in the detection, and (iii) comparing the estimation accuracy when the reflectance or the first derivative reflectance (FDR) is employed. Three data sets of useful spectral reflectance measurements in the 440 to 850 nm range were employed; measurements were taken over the leaves and canopy of bean crops with different levels of disease. A system based in Principal Component Analysis (PCA) and Artificial Neural Networks (ANN) was developed to estimate the disease severity from leaf and canopy hyperspectral reflectance spectra. Levels of disease to be taken as true reference were determined from the proportion of the total leaf surface covered by necrotic lesions on RGB images. When estimating ALS disease severity in bean crops by using hyperspectral reflectance spectrometry, this study suggests that (i) successful estimations with coefficients of determination up to 0.87 can be achieved if the spectra is acquired by the spectroradiometer in contact with the leaves, (ii) unsuccessful estimations are obtained when the spectra are acquired by the spectroradiometer from one or more meters above the crop, (iii) the red to near-infrared spectral region (630–850 nm) offers the same precision in the estimation as the blue to near-infrared spectral region (440–850), and (iv) neither significant improvements nor significant detriments are achieved when the input data to the estimation processing system are the FDR spectra, instead of the reflectance spectra.

R E S U M ONeste estudo objetivou-se identificar comprimentos de onda e faixas espectrais provenientes de reflectâncias hiper e multiespectrais utilizando regressão PLS e promover avaliação comparativa desses métodos e de dez índices de vegetação, para determinar aqueles que melhor estimam níveis de severidade de mofo-branco em feijão. Foram implantados experimentos nos municípios de Viçosa e de Oratórios, estado de Minas Gerais. Reflectâncias hiperespectrais foram obtidas com espectroradiômetro cuja faixa útil de leitura adotada foi entre 440 e 900 nm. Reflectâncias multiespectrais foram obtidas de imagens de câmara constituídas de cinco bandas (vermelho, verde, azul, Red-edge e infravermelho). Os índices de severidade da doença foram baixos; em Viçosa a média foi de 5,8% e em Oratórios, 7,4%. Modelos matemáticos utilizando reflectâncias hiperespectrais tiveram melhor desempenho para estimar mofo-branco; a banda do red-edge apresentou os comprimentos de onda que melhor estimam a severidade do mofo-branco. Índices de vegetação resistentes a efeitos da reflectância de solo estimaram melhor o mofo-branco do que os demais índices.Estimative of white mold severity in common bean crops using hyper and multispectral sensors A B S T R A C TThis study aimed to identify wavelengths and spectral ranges from hyper and multispectral reflectance using PLS regression; and to promote comparative evaluation of these methods and ten vegetation indices to determine those that best estimate levels of white mold severity in common beans. Experiments were implemented in the municipalities of Viçosa and Oratorios, Minas Gerais state. Hyperspectral reflectance measurements were acquired with the spectroradiometer, whose useful reading range was between 440 and 900 nm. Multispectral reflectance measurements were obtained from camera images comprising five bands (red, green, blue, red-edge and infrared). The indexes of disease severity were low. In Viçosa the average was 5.8% and in Oratorios, 7.4%. Mathematical models using hyperspectral reflectance performed better for estimating white mold. The red-edge band presented the wavelengths that best estimate the severity of white mold. Vegetation index resistant to the soil reflectance effects was better to estimate white mold than the other indices. Palavras-chave:regressão PLS índice de vegetação agricultura de precisão

The objective of this study is to evaluate the water conditions in a coffee plantation using precision agriculture (PA) techniques associated with geostatistics and high-resolution images. The study area is 1.2 ha of coffee crops of the Topázio MG 1190 cultivar. Two data collections were performed: one in the dry season and one in the rainy season. A total of 30 plants were marked and georeferenced within the study area. High-resolution images were obtained using a remotely piloted aircraft (RPA) equipped with a multispectral sensor. Leaf water potential was obtained using a Scholander pump. The spatialization and interpolation of the leaf water potential data were performed by geostatistical analysis. The vegetation indices were calculated through the images obtained by the RPA and were used for a regression and correlation analysis, together with the water potential data. The degree of spatial dependence (DSD) obtained by the geostatistical data showed strong spatial dependence for both periods evaluated. In the correlation analysis and linear regression, only the red band showed a significant correlation (39.93%) with an R² of 15.95%. The geostatistical analysis was an important tool for the spatialization of the water potential variable; conversely, the use of vegetation indexes obtained by the RPA was not as efficient in the evaluation of the water conditions of the coffee plants.

The identification of stress conditions in soybean crops is, in most cases, inaccurate, since they may not be noticeable to their full extent if only walking observations are carried out in the crop fields. This study aimed to identify the stress conditions in soybean crops, in three growing environments, in the Minas Gerais state, Brazil, using image processing techniques obtained by UAV, leaf and soil sensors, and climate data. The surveys encompassed two growth stages [beginning of blooming (R1) and beginning of seed enlargement (R5)] and consisted on UAV flights; mapping of chlorophyll content, soil moisture and soil pH; in addition to climate data. The HSV and yCbCr color models applied to RGB images showed the best Kappa accuracy index for the identification of crop features. The soil pH and moisture (water availability), solar radiation and temperature affected the crop growth and development in the study regions, in the R1 and R5 reproductive stages. However, the soil pH had less influence than the climatic variables. The R5 stage showed a greater vulnerability to stress caused by soil moisture and temperature.