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

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 limit...

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Bibliographic Details
Published in:	PeerJ. Computer science Vol. 10; p. e1828
Main Authors:	Alotaibi, Youseef, Rajendran, Brindha, Rani K., Geetha, Rajendran, Surendran
Format:	Journal Article
Language:	English
Published:	United States PeerJ. Ltd 25.01.2024 PeerJ, Inc PeerJ Inc
Subjects:	Accuracy Agricultural production Algorithms Algorithms and Analysis of Algorithms Artificial Intelligence Artificial neural networks Chlorophyll Classification Computational linguistics Crop classification Crop diseases Crops Data mining Data Mining and Machine Learning Datasets Deep learning Dipper throat optimization algorithm Dipping Environmental monitoring Feature extraction Food supply Image analysis Image processing Image resolution Language processing Literature reviews Machine learning Mathematical optimization Medical imaging equipment Methods Natural language interfaces Neural Networks Optimization Parameters Performance evaluation Plant diseases Remote sensing Remote sensing images Semantics Trigonometric functions Deep learning Crop classification Remote sensing images Image processing Dipper throat optimization algorithm
ISSN:	2376-5992, 2376-5992
Online Access:	Get full text
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