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

• 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

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...