Effective shadow detection and shadow removal can improve the performance of fruit recognition in natural environments and provide technical support for agricultural intelligence. In this study, a superpixel segmentation method was used to divide an image into multiple small regions. Based on the superpixel segmentation results, the shadow regions and the shadowless regions of the orchard images under natural light were compared and studied. Seven shadow saliency features (SSF) were explored and analyzed for shadow detection. The SSF were used to enhance the shadow characteristics. Then, the genetic algorithm (GA) was used to optimize the parameters, and support vector machine recursive feature elimination (SVM-RFE) was used to determine the best feature combination for shadow detection. According to the best feature combination, the support vector machine (SVM) algorithm was used to determine whether each segment of the superpixel segmentation results belonged to the shadow region. Shadow removal was carried out on each detected shadow region, and a natural light image after shadow removal was obtained. Finally, the accuracy of shadow detection was tested. The experimental results showed that the average accuracy of the shadow detection algorithm in this study was 91.91%. As a result, the precision and recall for fruits recognition after shadow removal generally improved.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Adankon, M. M., & Cheriet, M. (2009). Support vector machine. In S. Z. Li & A. Jain (Eds.), Encyclopedia of biometrics (pp. 1303–1308). Boston, MA, USA: Springer.
Baba, M., Mukunoki, M., & Asada, N. (2004). Shadow removal from a real image based on shadow density. In Proceedings of the ACM SIGGRAPH (pp. 8–12). New York, USA: ACM.
Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology,2(3), 1–27. https://doi.org/10.1145/1961189.1961199.
Comaniciu, D., & Meer, P. (2002). Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence,24(5), 603–619. https://doi.org/10.1109/34.1000236.
Finlayson, G. D., Drew, M. S., & Lu, C. (2004). Intrinsic images by entropy minimization. In T. Pajdla & J. Matas (Eds.), Computer vision (ECCV) (pp. 582–595). Berlin, Heidelberg, Germany: Springer.
Finlayson, G. D., Drew, M. S., & Lu, C. (2009). Entropy minimization for shadow removal. International Journal of Computer Vision,85(1), 35–57. https://doi.org/10.1007/s11263-009-0243-z.
Finlayson, G., Fredembach, C., & Drew, M. S. (2007). Detecting illumination in images. In 2007 IEEE 11th international conference on computer vision (ICCV) (pp. 1–8). Los Alamitos, CA, USA: IEEE.
Finlayson, G. D., Hordley, S. D., & Drew, M. S. (2002). Removing shadows from images. In A. Heyden, G. Sparr, M. Nielsen, & P. Johansen (Eds.), Computer vision (ECCV) (pp. 823–836). Berlin, Heidelberg, Germany: Springer.
Finlayson, G. D., Hordley, S. D., Lu, C., & Drew, M. S. (2006). On the removal of shadows from images. IEEE Transactions on Pattern Analysis and Machine Intelligence,28(1), 59–68. https://doi.org/10.1109/tpami.2006.18.
Freeman, W. T., Pasztor, E. C., & Carmichael, O. T. (2000). Learning low-level vision. International Journal of Computer Vision,40(1), 25–47. https://doi.org/10.1023/A:1026501619075.
Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc.
Gong, H., & Cosker, D. (2017). User-assisted image shadow removal. Image and Vision Computing,62, 19–27. https://doi.org/10.1016/j.imavis.2017.04.001.
Gonzalez, R. C., & Woods, R. E. (2007). Digital image processing (3rd ed.). Upper Saddle River, NJ, USA: Prentice-Hall Inc.
Gonzalez, R. C., Woods, R. E., & Eddins, S. L. (2003). Digital image processing using MATLAB. Upper Saddle River, NJ, USA: Prentice-Hall Inc.
Guo, R. Q., Dai, Q. Y., & Hoiem, D. (2013). Paired regions for shadow detection and removal. IEEE Transactions on Pattern Analysis and Machine Intelligence,35(12), 2956–2967. https://doi.org/10.1109/tpami.2012.214.
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning,46(1–3), 389–422. https://doi.org/10.1023/a:1012487302797.
Hertog, W., Llenas, A., & Carreras, J. (2015). Optimizing indoor illumination quality and energy efficiency using a spectrally tunable lighting system to augment natural daylight. Optics Express,23(24), 1564–1574. https://doi.org/10.1364/oe.23.01564.
Jobson, D. J., Rahman, Z., & Woodell, G. A. (1997). A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Transactions on Image Processing,6(7), 965–976. https://doi.org/10.1109/83.597272.
Khan, S. H., Bennamoun, M., Sohel, F., & Togneri, R. (2016). Automatic shadow detection and removal from a single image. IEEE Transactions on Pattern Analysis and Machine Intelligence,38(3), 431–446. https://doi.org/10.1109/tpami.2015.2462355.
Lalonde, J. F., Efros, A. A., & Narasimhan, S. G. (2010). Detecting ground shadows in outdoor consumer photographs. In K. Daniilidis, P. Maragos, & N. Paragios (Eds.), Computer vision (ECCV) (Vol. 6312, Lecture Notes in Computer Science, pp. 322–335). Berlin, Heidelberg, Germany: Springer.
Lei, T., Jia, X. H., Zhang, Y. N., He, L. F., Meng, H. Y., & Nandi, A. K. (2018). Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering. IEEE Transactions on Fuzzy Systems,26(5), 3027–3041. https://doi.org/10.1109/tfuzz.2018.2796074.
Levine, M. D., & Bhattacharyya, J. (2005). Removing shadows. Pattern Recognition Letters,26(3), 251–265. https://doi.org/10.1016/j.patrec.2004.10.021.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth berkeley symposium on mathematical statistics and probability (pp. 281–297). Berkeley, CA, USA: University of California Press.
Matsushita, Y., Nishino, K., Ikeuchi, K., & Sakauchi, M. (2004). Illumination normalization with time-dependent intrinsic images for video surveillance. IEEE Transactions on Pattern Analysis and Machine Intelligence,26(10), 1336–1347. https://doi.org/10.1109/tpami.2004.86.
Mo, N., Zhu, R. X., Yan, L., & Zhao, Z. (2018). Deshadowing of urban airborne imagery based on object-oriented automatic shadow detection and regional matching compensation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,11(2), 585–605. https://doi.org/10.1109/jstars.2017.2787116.
Otsu, N. (1979). Threshold selection method from gray-level histograms. IEEE Transactions on Systems Man and Cybernetics,9(1), 62–66. https://doi.org/10.1109/tsmc.1979.4310076.
Phong, B. T. (1975). Illumination for computer generated pictures. Communications of the ACM,18(6), 311–317. https://doi.org/10.1145/360825.360839.
Shen, L., & Yeo, C. (2011). Intrinsic images decomposition using a local and global sparse representation of reflectance. In 2011 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 697–704). Los Alamitos, CA, USA: IEEE.
Shen, L., Yeo, C., & Hua, B.-S. (2013). Intrinsic image decomposition using a sparse representation of reflectance. IEEE Transactions on Pattern Analysis and Machine Intelligence,35(12), 2904–2915. https://doi.org/10.1109/tpami.2013.136.
Suh, H. K., Hofstee, J. W., & van Henten, E. J. (2018). Improved vegetation segmentation with ground shadow removal using an HDR camera. Precision Agriculture,19(2), 218–237. https://doi.org/10.1007/s11119-017-9511-z.
Sun, J., Tian, J. D., Du, Y. K., & Tang, Y. D. (2009). Retinex theory-based shadow detection and removal in single outdoor image. Industrial Robot: An International Journal,36(3), 263–269. https://doi.org/10.1108/01439910910950531.
Wang, G., Wei, Y., & Qiao, S. (2018). Equation solving generalized inverses. In G. Wang, Y. Wei, & S. Qiao (Eds.), Generalized inverses: Theory and computations (pp. 1–64). Singapore: Springer.
Wang, S.-T., Yuan, Y.-Y., Zhu, C.-Y., Kong, D.-M., & Wang, Y.-T. (2019). Discrimination of polycyclic aromatic hydrocarbons based on fluorescence spectrometry coupled with CS-SVM. Measurement,139, 475–481. https://doi.org/10.1016/j.measurement.2019.01.087.
Weiss, Y. (2001). Deriving intrinsic images from image sequences. In Proceedings eighth IEEE international conference on computer vision (ICCV 2001) (pp. 68–75). Los Alamitos, CA, USA: IEEE. https://doi.org/10.1109/iccv.2001.937606.
Wu, T. P., & Tang, C. K. (2005). A Bayesian approach for shadow extraction from a single image. In Tenth IEEE international conference on computer vision (ICCV) (pp. 480–487). Los Alamitos, CA, USA: IEEE.
Zhu, J. J., Samuel, K. G. G., Masood, S. Z., & Tappen, M. F. (2010). Learning to recognize shadows in monochromatic natural images. In 2010 IEEE conference on computer vision and pattern recognition (pp. 223–230). Los Alamitos, CA, USA: IEEE.
This work is supported by the Natural Science Foundation of Guangdong (No. 2018A030313330), the Special Funds for the Cultivation of Guangdong College Students’ Scientific and Technological Innovation (“Climbing Program” Special Funds.) (pdjh2019a0073), the National Natural Science Foundation of China (Nos. 31201135, 31571568) and the Science and Technology Plan Project of Guangzhou (201802020032). The authors wish to thank the useful comments of the anonymous reviewers to this paper.
Conflict of interest
The authors declare that they have no conflicts of interest.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Bu, R., Xiong, J., Chen, S. et al. A shadow detection and removal method for fruit recognition in natural environments. Precision Agric 21, 782–801 (2020). https://doi.org/10.1007/s11119-019-09695-1
- Shadow detection
- Fruit detection
- Feature extraction
- Shadow removal
- Support vector machine