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Open AccessArticle

Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm

by Alwaseela Abdalla 1,2,3,4, Haiyan Cen 1,2,3,*, Elfatih Abdel-Rahman 5,6, Liang Wan 1,2,3 and Yong He 1,2,3
1
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
2
Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
3
State Key laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China
4
Agricultural Research Corporation, P.O. Box 126, Wad Medani 11111, Sudan
5
International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, Kenya
6
Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(24), 3001; https://doi.org/10.3390/rs11243001
Received: 16 October 2019 / Revised: 6 December 2019 / Accepted: 6 December 2019 / Published: 13 December 2019
(This article belongs to the Special Issue Advanced Imaging for Plant Phenotyping)
Plant color is a key feature for estimating parameters of the plant grown under different conditions using remote sensing images. In this case, the variation in plant color should be only due to the influence of the growing conditions and not due to external confounding factors like a light source. Hence, the impact of the light source in plant color should be alleviated using color calibration algorithms. This study aims to develop an efficient, robust, and cutting-edge approach for automatic color calibration of three-band (red green blue: RGB) images. Specifically, we combined the k-means model and deep learning for accurate color calibration matrix (CCM) estimation. A dataset of 3150 RGB images for oilseed rape was collected by a proximal sensing technique under varying illumination conditions and used to train, validate, and test our proposed framework. Firstly, we manually derived CCMs by mapping RGB color values of each patch of a color chart obtained in an image to standard RGB (sRGB) color values of that chart. Secondly, we grouped the images into clusters according to the CCM assigned to each image using the unsupervised k-means algorithm. Thirdly, the images with the new cluster labels were used to train and validate the deep learning convolutional neural network (CNN) algorithm for an automatic CCM estimation. Finally, the estimated CCM was applied to the input image to obtain an image with a calibrated color. The performance of our model for estimating CCM was evaluated using the Euclidean distance between the standard and the estimated color values of the test dataset. The experimental results showed that our deep learning framework can efficiently extract useful low-level features for discriminating images with inconsistent colors and achieved overall training and validation accuracies of 98.00% and 98.53%, respectively. Further, the final CCM provided an average Euclidean distance of 16.23 ΔΕ and outperformed the previously reported methods. This proposed technique can be used in real-time plant phenotyping at multiscale levels. View Full-Text
Keywords: color calibration; deep learning; k-means algorithm; plant phenotyping; multivariate regression color calibration; deep learning; k-means algorithm; plant phenotyping; multivariate regression
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MDPI and ACS Style

Abdalla, A.; Cen, H.; Abdel-Rahman, E.; Wan, L.; He, Y. Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm. Remote Sens. 2019, 11, 3001.

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