Improved chimp optimization with deep transfer learning enabled soil classification technique using hyperspectral remote sensing images

Abstract

Hyperspectral remote sensing (HRS) is commonly employed for crop and soil mapping. Soil and crop classification mapping acts as major roles in planning and management of sustainable development. Thus, it is necessary to design automated crop and soil prediction techniques using HRS images. The recent developments of machine learning (ML) and deep learning (DL) enable to design of effective classification models for soil and crops. This study effectively introduces improved chimp optimization with deep transfer learning enabled crop and soil classification (ICODTL-CSC) technique using HRS images. The presented ICODTL-SC technique aims to identify and categorize different types of crops and soil. To accomplish this, the ICODTL-CSC technique incorporates Adagrad with EffcientNetB0-based feature extractor for producing a collection of feature vectors. In addition, Improved Chimp Optimization Algorithm with a deep adaptive wavelet network model is applied for soil classification, showing the novelty of our work. The performance validation of the presented method is performed using Tamil Nadu Hills dataset, Pavia University, and Salinas scene datasets. The experimental outcomes indicated that the presented method has exhibited the existing techniques in terms of different performance measures.