Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms

• Published in: PeerJ. Computer science Vol. 7; p. e536
• Main Authors: Iqbal, Naveed, Mumtaz, Rafia, Shafi, Uferah, Zaidi, Syed Mohammad Hassan
• Format: Journal Article
• Language: English
• Published: San Diego PeerJ. Ltd 19.05.2021; PeerJ, Inc; PeerJ Inc
• Subjects: Accuracy; Algorithms; Altitude; Cable television broadcasting industry; Classification; Computer Vision; Corn; Crops; Data Mining and Machine Learning; Data Science; Decision trees; Deep learning; Feature extraction; Gray scale; Image classification; Image resolution; Low altitude; Machine learning; Neural networks; Platforms; Principal components analysis; Remote sensing; Satellites; Smartphones; Spatial and Geographic Information Systems; Support vector machines; Texture analysis; Time series; Trends; Unmanned aerial vehicles; Wheat; United States; Pakistan

Crop classification in early phenological stages has been a difficult task due to spectrum similarity of different crops. For this purpose, low altitude platforms such as drones have great potential to provide high resolution optical imagery where Machine Learning (ML) applied to classify different...