Soil Morphology Based on Deep Learning, Polynomial Learning and Gabor Teager-Kaiser Energy Operators

Soil Morphology is considered the main observable characteristics of the different soil horizons. It helps farmers to determine what kind of soil they can use for their different plants. The observable characteristics include soil structure, color, distribution of roots and pores. The main concept of this chapter is to classify the different soils based on their morphology. Furthermore, the chapter contains a comparison between polynomial neural network and deep learning for soil classification. The chapter introduces a background about the different methods of feature extraction including the Gabor wavelet transform, Teager-Kaiser operator, deep learning, and polynomial neural networks. The chapter, also, includes two 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 different morphological soil based on two methods: deep learning and polynomial neural network. We achieved accuracy limits of (95–100%) for the polynomial and deep learning classification achieved accuracy up to 95% but the deep learning is more accurate and very powerful. Finally, we compare our work results with the previous work and research. Results show an accuracy range of (98–100%) for our work compared with (95.1–98.8%) for the previous algorithms based on PNN. Furthermore, the accuracy of using DNN in this chapter comparing with pervious works achieved a good accuracy rather than the others.