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

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 consist

SUBMITTER: Alotaibi Y 

PROVIDER: S-EPMC10909238 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature