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Applying Polynomial Learning for Soil Detection Based on Gabor Wavelet and Teager Kaiser Energy Operator

  • Kamel H. RahoumaEmail author
  • Rabab Hamed M. Aly
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)

Abstract

Soil detection is playing an important role in the environmental research. It helps the farmers to determine what kind of plants they can have. Also, it may help to mix plants in certain areas or farm new types. The main target of this paper is to classify the different types of soil. On the other hand, there are many researches which focus on the classification and detection process based on different applications of image processing and computer vision. The paper has two main goals. The first goal is to improve the extraction of soil features based on Gabor wavelet transform but followed by the Teager-Kaiser Operator. The second goal is to classify the types of soil based on group method data handling (polynomial neural networks). We applied these methods using different data sets of soil. Compared with previous work and research, we achieved accuracy limits of (98%–100%) while the previous algorithms were accurate to the limits of (95.1%–98.8%). Behind this improvement in accuracy, there are the methods we used here including the Teager Kaiser operator with Gabor wavelet and polynomial neural networks which have been proved to be more accurate than the methods used before.

Keywords

Soil detection Gabor wavelet Polynomial neural network (PNN) Teager-Kaiser 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Electrical Engineering Department, Faculty of EngineeringMinia UniversityMiniaEgypt
  2. 2.The Higher Institute for Management Technology and InformationMiniaEgypt

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