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
• DOI: 10.7717/peerj-cs.536
• ISSN: 2376-5992

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 types of crops. In this research work, crop classification is performed at different phenological stages using optical images which are obtained from drone. For this purpose, gray level co-occurrence matrix (GLCM) based features are extracted from underlying gray scale images collected by the drone. To classify the different types of crops, different ML algorithms including Random Forest (RF), Naive Bayes (NB), Neural Network (NN) and Support Vector Machine (SVM) are applied. The results showed that the ML algorithms performed much better on GLCM features as compared to gray scale images with a margin of 13.65% in overall accuracy.