Advances in Manufacturing

, Volume 6, Issue 4, pp 409–418 | Cite as

Hidden feature extraction for unstructured agricultural environment based on supervised kernel locally linear embedding modeling

  • Zhong-Hua Miao
  • Chen-Hui Ma
  • Zhi-Yuan GaoEmail author
  • Ming-Jun Wang
  • Cheng-Liang Liu


An online hidden feature extraction algorithm is proposed for unknown and unstructured agricultural environments based on a supervised kernel locally linear embedding (SKLLE) algorithm. Firstly, an online obtaining method for scene training samples is given to obtain original feature data. Secondly, Bayesian estimation of the a posteriori probability of a cluster center is performed. Thirdly, nonlinear kernel mapping function construction is employed to map the original feature data to hyper-high-dimensional kernel space. Fourthly, the automatic determination of hidden feature dimensions is performed using a local manifold learning algorithm. Then, a low-level manifold computation in hidden space is completed. Finally, long-range scene perception is realized using a 1-NN classifier. Experiments are conducted to show the effectiveness and the influence of parameter selection for the proposed algorithm. The kernel principal component analysis (KPCA), locally linear embedding (LLE), and supervised locally linear embedding (SLLE) methods are compared under the same experimental unstructured agricultural environment scene. Test results show that the proposed algorithm is more suitable for unstructured agricultural environments than other existing methods, and that the computational load is significantly reduced.


Long-range scene perception Hidden feature extraction Agricultural vehicle Unstructured agricultural environment 



This paper was sponsored by the National Natural Science Foundation of China (Grant No. 51375293) and the Basic Research of the Science and Technology Commission of Shanghai Municipality (Grant No. 12JC1404100).


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Copyright information

© Shanghai University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechatronic Engineering and AutomationShanghai UniversityShanghaiPeople’s Republic of China
  2. 2.School of Mechanical EngineeringShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China

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