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Análise de imagem para determinação do teor de saponina em quinoa PAB
Souza,Luiz Augusto Copati; Spehar,Carlos Roberto; Santos,Roberto Lorena Bastos.
Um grupo de sementes lavadas e 35 acessos de quinoa (Chenopodium quinoa Willd) foram avaliados pelo método de coluna de espuma e sua coloração foi decomposta pelo modelo RGB (R, vermelho; G, verde; B, azul) com o objetivo de avaliar a influência do teor de saponina na cor do grão. Sementes amarelas apresentaram alto teor de saponina. Houve correlação negativa (p£0,05) entre o teste de coluna de espuma e as bandas R (r = -0,751), G (r = -0,660) e B (r = -0,594). Estabeleceram-se quatro grupos de similaridade. Foram considerados amargos os acessos do grupo 4 (sementes amarelas) e doces os acessos do grupo 1 (sementes brancas). A dispersão observada representa provável diferença na freqüência gênica, refletida pela cor e teor de saponina.
Tipo: Info:eu-repo/semantics/report Palavras-chave: RGB; Genótipo; Glicosídeo; Correlação; Análise multivariada.
Ano: 2004 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2004000400014
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Computer vision system approach in colour measurements of foods: Part I. development of methodology Ciênc. Tecnol. Aliment.
TARLAK,Fatih; OZDEMİR,Murat; MELİKOGLU,Mehmet.
Abstract The colour assessment ability of the computer vision system is investigated and the data are compared with colour measurements taken by a conventional colorimeter. Linear and quadratic models are built to improve currently used methodology for the conversion of RGB colour units to L * a * b * colour space. For this purpose, two innovative ideas are proposed and tested. First, substantial amount of colour tones is generated to cover as many points in the colour space as possible. Secondly, the colour space is calibrated separately, whereas in previous research in the literature, the colour space is calibrated simultaneously. It is found that the RGB colour units to L * a * b * colour space transformation approach proposed in this study...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Colour; Computer vision system; Colorimeter; RGB; L* a* b*.
Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612016000200382
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Computer vision system approach in colour measurements of foods: Part II. validation of methodology with real foods Ciênc. Tecnol. Aliment.
TARLAK,Fatih; OZDEMİR,Murat; MELİKOGLU,Mehmet.
Abstract The colour of food is one of the most important factors affecting consumers’ purchasing decision. Although there are many colour spaces, the most widely used colour space in the food industry is L*a*b* colour space. Conventionally, the colour of foods is analysed with a colorimeter that measures small and non-representative areas of the food and the measurements usually vary depending on the point where the measurement is taken. This leads to the development of alternative colour analysis techniques. In this work, a simple and alternative method to measure the colour of foods known as “computer vision system” is presented and justified. With the aid of the computer vision system, foods that are homogenous and uniform in colour and shape could be...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Computer vision system; Food; RGB; L*a*b*.
Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0101-20612016000300499
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Deficiencias de hierro y manganeso en hojas de frijol (Phaseolus vulgaris L.) identificadas mendiante análisis textural, color de imágenes digitales y redes neuronales artificiales. Colegio de Postgraduados
García Cruz, Edgar.
En la presente investigación se analizaron imágenes digitales de hojas de frijol (Phaseolus vulgaris L.) para identificar con un clasificador, deficiencias de hierro (Fe) y manganeso (Mn). A los 24 días después de la siembra (dds) se les suministró la solución nutritiva de acuerdo a ocho tratamientos: dos deficiencias parciales, una de 50 % Fe y otra de 50 % Mn; dos deficiencias totales totales, 0 % Fe y una más de 0 % Mn además de una interacción (0 % Fe, 0 % Mn) y dos dosis excedentes (200 % Fe y 200 % Mn); finalmente un tratamiento testigo (100 % Fe, 100 % Mn) usando como referencia la solución Steiner. A partir de imágenes digitales de muestras de hojas de los tratamientos obtenidas a los 63 dds, se calcularon variables de color con los valores...
Palavras-chave: RGB; Textura; Redes neuronales; Phaseolus vulgaris; Hierro; Manganeso; Texture; Neural networks; Iron; Manganese; Edafología; Maestría.
Ano: 2013 URL: http://hdl.handle.net/10521/2076
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IDENTIFICATION OF MAIZE LODGING: A CASE STUDY USING A REMOTELY PILOTED AIRCRAFT SYSTEM REA
Acorsi,Matheus G.; Martello,Maurício; Angnes,Graciele.
ABSTRACT A common agricultural problem in many regions of Brazil is maize lodging, as a consequence of strong winds and rain which impacts on crop growth and yield. However, collecting data using ground-based, manual field measurement methods is inefficient. An emerging tool is the Remotely Piloted Aircraft System (RPAS), capable of delivering spatial data with high resolution and flexible periodicity. In this study, the potential to detect the maize lodging using crop surface models derived from RPAS was assessed. Our RPA-based approach uses a quantitative threshold to determine lodging percentage. The threshold values of plant height, used to detect the occurrence of lodging, were based on fixed and variable values. The validation of percentage lodging...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Crop surface model; Structure from motion; Canopy height; RGB.
Ano: 2019 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-69162019000800066
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Mapping key indicators of forest restoration in the Amazon using a low-cost drone and artificial intelligence. Repositório Alice
ALBUQUERQUE, R. W.; VIEIRA, D. L. M.; FERREIRA, M. E.; SOARES, L. P.; OLSEN, S. I.; ARAUJO, L. S. de; VICENTE, L. E.; TYMUS, J. R. C.; BALIEIRO, C. P.; MATSUMOTO, M. H.; GROHMANN, C. H..
Na publicação: Luciana Spinelli Araujo.
Tipo: Artigo de periódico Palavras-chave: Deep learning; Drones; Remotely piloted aircraft; RGB; Tree crown heterogeneity index; Tree species; Cecropia; Photogrammetry; Species diversity; Vismia.
Ano: 2022 URL: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1140126
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Use of digital images to estimate soil moisture AGRIAMBI
Santos,João F. C. dos; Silva,Heider R. F.; Pinto,Francisco A. C.; Assis,Igor R. de.
ABSTRACT The objective of this study was to analyze the relation between the moisture and the spectral response of the soil to generate prediction models. Samples with different moisture contents were prepared and photographed. The photographs were taken under homogeneous light condition and with previous correction for the white balance of the digital photograph camera. The images were processed for extraction of the median values in the Red, Green and Blue bands of the RGB color space; Hue, Saturation and Value of the HSV color space; and values of the digital numbers of a panchromatic image obtained from the RGB bands. The moisture of the samples was determined with the thermogravimetric method. Regression models were evaluated for each image type: RGB,...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Soil color; Image processing; RGB; HSV.
Ano: 2016 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1415-43662016001201051
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Use of image analysis for monitoring the dilution of Physalis peruviana pulp BABT
Licodiedoff,Silvana; Ribani,Rosemary Hoffmann; Camlofski,Ana Mery de Oliveira; Lenzi,Marcelo Kaminski.
The aim of this work was to develop linear models using the image analysis coupled with density measurements to monitor the dilution of the Physalis juice in the concentrations ranging from 0 to 100% in mass of juice pulp. A sample corresponding to 20% in the mass of juice pulp was for validating purposes and a prediction of 19.9±0.3%. The models with three parameters showed the best predictions, providing this technique with a promising future for the monitoring the dilution of fruit juices.
Tipo: Info:eu-repo/semantics/article Palavras-chave: Juice; Fruit; Color; Image; RGB; Physalis.
Ano: 2013 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132013000300015
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