Alotaibi Youseef, Rajendran Brindha, Rani K Geetha, Rajendran Surendran
College of Computer and Information Systems, Umm Al Qura University, Makkah, Saudi Arabia.
Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, India.
PeerJ Comput Sci. 2024 Jan 25;10:e1828. doi: 10.7717/peerj-cs.1828. eCollection 2024.
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.
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.
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.
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.
随着遥感技术的迅速发展，对高效且准确的作物分类方法的需求变得越来越重要。这是由于对粮食安全和环境监测的需求不断增长。传统的作物分类方法在准确性和可扩展性方面存在局限性，尤其是在处理高分辨率遥感图像的大型数据集时。本研究旨在开发一种名为基于深度卷积神经网络的作物分类的勺喉优化（DTODCNN-CC）的新型作物分类技术，用于分析遥感图像。目标是实现对各种粮食作物的高分类准确率。
所提出的DTODCNN-CC方法由以下关键组件组成。深度卷积神经网络（DCNN）采用GoogleNet架构从遥感图像中提取强大的特征向量。勺喉优化（DTO）优化器用于GoogleNet模型的超参数调整，以实现最佳特征提取性能。极限学习机（ELM）：这种机器学习算法用于基于提取的特征对不同的粮食作物进行分类。改进的正弦余弦算法（MSCA）优化技术用于微调ELM的参数，以提高分类准确率。
进行了广泛的实验分析，以评估所提出的DTODCNN-CC方法的性能。结果表明，与其他现有先进的深度学习方法相比，DTODCNN-CC可以实现显著更高的作物分类准确率。
所提出的DTODCNN-CC技术为使用遥感图像进行高效且准确的作物分类提供了一个有前景的解决方案。这种方法有可能成为农业、粮食安全和环境监测等各种应用中的宝贵工具。
