Feature extraction of hyperspectral images for detecting immature green citrus fruit

Abstract

At an early immature growth stage of citrus, a hyperspectral camera of 369–1042 nm was employed to acquire 30 hyperspectral images in order to detect immature green fruit within citrus trees under natural illumination conditions. First, successive projections algorithm (SPA) were implemented to select 677, 804, 563, 962, and 405 nm wavebands and to construct multispectral images from the original hyperspectral images for further processing. Then, histogram threshold segmentation using NDVI of 804 and 677 nm was implemented to remove image backgrounds. Three slope parameters, calculated from the pairs 405 and 563 nm, 563 and 677 nm, and 804 and 962 nm were used to construct a classifier to identify the potential citrus fruit. Then, a marker-controlled watershed segmentation based on wavelet transform was applied to obtain potential fruit areas. Finally, a green fruit detection model was constructed according to Grey Level Co-occurrence Matrix (GLCM) texture features of the independent areas. Three supervised classifiers, logistic regression, random forest and support vector machine (SVM) were developed using texture features. The detection accuracies were 79%, 75%, and 86% for the logistic regression, random forest, and SVM models, respectively. The developed algorithm showed a great potential for identifying immature green citrus for an early yield estimation.

Keywords

hyperspectral / green citrus / image processing / fruit detection / precision agriculture / yield mapping

References

[1] Bulanon D M, Burks T F, Alchanatis V, Noguchi N. A multispectral imaging analysis for enhancing citrus fruit detection. Environment Control in Biology, 2010, 48(2): 81–91

[2] Bulanon D M, Burks T F, Alchanatis V. Study on temporal variation in citrus canopy using thermal imaging for citrus fruit detection. Biosystems Engineering, 2008, 101(2): 161–171

[3] Xu H R, Ye Z Z, Ying Y B. Identification of citrus fruit in a tree canopy using color information. Transactions of the Chinese Society of Agricultural Engineering, 2005, 21(5): 98–101 (in Chinese)

[4] Cai J R, Zhou X J, Li Y L, Fan J. Recognition of mature oranges in natural scene based on machine vision. Transactions of the Chinese Society of Agricultural Engineering, 2008, 24(1): 175–178 (in Chinese)

[5] Kane K E, Lee W S. Multispectral imaging for in-field green citrus identification. In: ASAE Annual Meeting 2007, Minneapolis. St. Joseph: American Society of Agricultural and Biological Engineers, 2007, 1–11

[6] Kurtulmus F, Lee W S, Vardar A. Green citrus detection using ‘eigenfruit’ color and circular Gabor texture features under natural outdoor conditions. Computers and Electronics in Agriculture, 2011, 78(2): 140–149

[7] Sengupta S, Lee W S. Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions. Biosystems Engineering, 2014, 117(1): 51–61

[8] Zhao C, Lee W S, He D. Immature green citrus detection based on colour feature and sum of absolute transformed difference (SATD) using colour images in the citrus grove. Computers and Electronics in Agriculture, 2016, 124(C): 243–253

[9] Li H, Lee W S, Wang K. Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images. Precision Agriculture, 2016, 17(6): 678–697

[10] Okamoto H, Lee W S. Green citrus detection using hyperspectral imaging. Computers and Electronics in Agriculture, 2009, 66(2): 201–208

[11] Kim M S, Chen Y R, Mehl P M. Hyperspectral reflectance and fluorescence imaging system for food quality and safety. Transactions of the ASAE (American Society of Agricultural Engineers), 2001, 44(3): 721–729

[12] Araújo M C U, Saldanha T C B, Galvao R K H, Yoneyama T, Chame H C, Visani V. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis. Chemometrics and Intelligent Laboratory Systems, 2001, 57(2): 65–73

[13] Wu P C, Chen L G. An efficient architecture for two-dimensional discrete wavelet transform. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(4): 536–545

[14] Kim J B, Kim H J. Multiresolution-based watersheds for efficient image segmentation. Pattern Recognition Letters, 2003, 24(1): 473–488

[15] Yang X, Li H, Zhou X. Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy. IEEE Transactions on Circuits and Systems. I, Regular Papers, 2006, 53(11): 2405–2414

[16] Yazdi M, Gheysari K. A new approach for the fingerprint classification based on gray-level co-occurrence matrix. International Journal of Computer and Information Science and Engineering, 2008, 2(3): 171–174

[17] Yan J, Lee J. Degradation assessment and fault modes classification using logistic regression. Journal of Manufacturing Science and Engineering, 2005, 127(4): 912–914

[18] Guo L, Ma Y, Cukic B, Singh H. Robust prediction of fault-proneness by random forests. In: 15th International Symposium on Software Reliability Engineering 2004, Bretagne. USA: IEEE, 2004, 417–428

[19] Cherkassky V, Ma Y. Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks, 2004, 17(1): 113–126

[20] Brereton R G, Lloyd G R. Support vector machines for classification and regression. Analyst, 2010, 135(2): 230–267

Acknowledgements

This work was funded by the National Natural Science Foundation of China (31360291), China Scholarship Council (201408625069) and University of Florida.

Compliance with ethics guidelines

ƒYongjun Ding, Won Suk Lee, and Minzan Li declare that they have no conflicts of interest or financial conflicts to disclose.ƒ

This article does not contain any studies with human or animal subjects performed by any of the authors.

RIGHTS & PERMISSIONS

The Author(s) 2018. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)