Dipper throated optimization with deep convolutional neural network-based crop classification for remote sensing image analysis

Youseef Alotaibi; Brindha Rajendran; Geetha Rani K; Surendran Rajendran

doi:10.7717/peerj-cs.1828

Dipper throated optimization with deep convolutional neural network-based crop classification for remote sensing image analysis

PeerJ Comput Sci. 2024 Jan 25:10:e1828. doi: 10.7717/peerj-cs.1828. eCollection 2024.

Authors

Youseef Alotaibi¹, Brindha Rajendran², Geetha Rani K³, Surendran Rajendran⁴

Affiliations

¹ College of Computer and Information Systems, Umm Al Qura University, Makkah, Saudi Arabia.
² Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, India.
³ Department of Computer Science and Engineering, Jain (Deemed-to-be University), Bangalore, India.
⁴ Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.

Abstract

Problem: With the rapid advancement of remote sensing technology is that the need for efficient and accurate crop classification methods has become increasingly important. This is due to the ever-growing demand for food security and environmental monitoring. Traditional crop classification methods have limitations in terms of accuracy and scalability, especially when dealing with large datasets of high-resolution remote sensing images. This study aims to develop a novel crop classification technique, named Dipper Throated Optimization with Deep Convolutional Neural Networks based Crop Classification (DTODCNN-CC) for analyzing remote sensing images. The objective is to achieve high classification accuracy for various food crops.

Methods: The proposed DTODCNN-CC approach consists of the following key components. Deep convolutional neural network (DCNN) a GoogleNet architecture is employed to extract robust feature vectors from the remote sensing images. The Dipper throated optimization (DTO) optimizer is used for hyper parameter tuning of the GoogleNet model to achieve optimal feature extraction performance. Extreme Learning Machine (ELM): This machine learning algorithm is utilized for the classification of different food crops based on the extracted features. The modified sine cosine algorithm (MSCA) optimization technique is used to fine-tune the parameters of ELM for improved classification accuracy.

Results: Extensive experimental analyses are conducted to evaluate the performance of the proposed DTODCNN-CC approach. The results demonstrate that DTODCNN-CC can achieve significantly higher crop classification accuracy compared to other state-of-the-art deep learning methods.

Conclusion: The proposed DTODCNN-CC technique provides a promising solution for efficient and accurate crop classification using remote sensing images. This approach has the potential to be a valuable tool for various applications in agriculture, food security, and environmental monitoring.

Keywords: Crop classification; Deep learning; Dipper throat optimization algorithm; Image processing; Remote sensing images.

Grants and funding

The current research study has received financial support from the Deanship for Research & Innovation, Ministry of Education in Saudi Arabia, under the auspices of project number: IFP22UQU4281768DSR120. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.