Optimal color space selection method for plant/soil segmentation in agriculture

Hernández-Hernández, José Luis; Garcı́a-Mateos, Ginés; González-Esquiva, J.M.; Escarabajal-Henarejos, D.; Ruiz-Canales, A.; Molina-Martínez, José Miguel

doi:10.1016/j.compag.2016.01.020

Cited by 103 publications

(71 citation statements)

References 27 publications

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“…Several studies have shown a better separation of the image features by weighting each channel differently during the process of transforming the RGB color space. Such changes are expected to yield diverse color distributions in each model, as most of the transformations are non-linear [32,33]. For example, in a study where 11 different color spaces (RGB, normalized rgb, XYZ, L*a*b*, L*u*v*, HSV, HLS, YCrCb, YUV, I1I2I3 and TSL) were compared to segment lettuce plants and soil from a set of images, the L*a*b* color space was shown to achieve superior classification, with 99.2% accuracy [34].…”

Section: Discussionmentioning

confidence: 99%

Supervised Classification of RGB Aerial Imagery to Evaluate the Impact of a Root Rot Disease

et al. 2018

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Aerial imaging provides a landscape view of crop fields that can be utilized to monitor plant diseases. Phymatotrichopsis root rot (PRR) is a serious root rot disease affecting several dicotyledonous hosts, including the perennial forage crop alfalfa. PRR disease causes stand loss by spreading as circular to irregular diseased areas that increase over time, but disease progression in alfalfa fields is poorly understood. The objectives of this study were to develop a workflow to produce PRR disease maps from sets of high-resolution red, green and blue (RGB) images acquired from two different platforms and to assess the feasibility of using these PRR disease maps to monitor disease progression in alfalfa fields. Aerial RGB images, two from unmanned aircraft systems (UAS) and four images from a manned aircraft platform were acquired at different time points during the 2014-2015 growing seasons from a center-pivot irrigated, PRR-infested alfalfa field near Burneyville, OK. Supervised classification of images acquired from both platforms were performed using three spectral signatures: image-specific, UAS-platform-specific and manned-aircraft platform-specific. Our results showed that the UAS-platform-specific spectral signature was most efficient for classifying images acquired with the UAS, with accuracy ranging from 90 to 96%. In contrast, manned-aircraft-acquired images classified using image-specific spectral signatures yielded 95 to 100% accuracy. The effect of hue, saturation and value color space transformations (HSV and Hrot60SV) on classification accuracy was determined, but the accuracy estimates showed no improvement in their efficiency compared to the RGB color space. Finally, the data showed that the classification of the bare ground increased by 74% during the study period, indicating the extent of alfalfa stand loss caused by PRR disease. Thus, this study showed the utility of high-resolution RGB aerial images for monitoring PRR disease spread in alfalfa.

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Section: Discussionmentioning

confidence: 99%

Supervised Classification of RGB Aerial Imagery to Evaluate the Impact of a Root Rot Disease

et al. 2018

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“…Em especial as influências da iluminação e a presença de sombras são as principais fontes de variações nas propriedades visíveis do objeto, tornando a detecção e identificação muito mais inconstantes e dificultosas, especialmente nos aspectos de cor. Para suplantar estes desafios, técnicas de processamento de imagens e algoritmos podem ser desenvolvidas e aprimoradas, visando eliminar os ruídos associados às condições ambiente e extrair apenas as informações úteis da imagem (TIAN; SLAUGHTER, 1998;HERNÁNDEZ-HERNÁNDEZ et al, 2016;HAMUDA;GLAVIN;JONES, 2016).…”

Section: "Sistema De Gerenciamento Agrícola Baseado Na Variação Espacunclassified

Análise experimental de algoritmos de constância de cor e segmentação para detecção de mudas de plantas

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“…Agronomy 2019, 9, 672 4 of 12 In order to segment the chickpeas with high accuracy, 5 color spaces were analyzed [18]. These color spaces are RGB, YIQ, HSV, HIS and YCbCr.…”

Section: Segmentation Of the Chickpeasmentioning

confidence: 99%

Automatic Classification of Chickpea Varieties Using Computer Vision Techniques

et al. 2019

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There are about 90 different varieties of chickpeas around the world. In Iran, where this study takes place, there are five species that are the most popular (Adel, Arman, Azad, Bevanij and Hashem), with different properties and prices. However, distinguishing them manually is difficult because they have very similar morphological characteristics. In this research, two different computer vision methods for the classification of the variety of chickpeas are proposed and compared. The images were captured with an industrial camera in Kermanshah, Iran. The first method is based on color and texture features extraction, followed by a selection of the most effective features, and classification with a hybrid of artificial neural networks and particle swarm optimization (ANN-PSO). The second method is not based on an explicit extraction of features; instead, image patches (RGB pixel values) are directly used as input for a three-layered backpropagation ANN. The first method achieved a correct classification rate (CCR) of 97.0%, while the second approach achieved a CCR of 99.3%. These results prove that visual classification of fruit varieties in agriculture can be done in a very precise way using a suitable method. Although both techniques are feasible, the second method is generic and more easily applicable to other types of crops, since it is not based on a set of given features.

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Optimal color space selection method for plant/soil segmentation in agriculture

Cited by 103 publications

References 27 publications

Supervised Classification of RGB Aerial Imagery to Evaluate the Impact of a Root Rot Disease

Supervised Classification of RGB Aerial Imagery to Evaluate the Impact of a Root Rot Disease

Análise experimental de algoritmos de constância de cor e segmentação para detecção de mudas de plantas

Automatic Classification of Chickpea Varieties Using Computer Vision Techniques

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