ArticlePeerJ. Computer science2021
Gray level co-occurrence matrix (GLCM) texture based crop classification using low altitude remote sensing platforms.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
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- Advanced computer-aided diagnosis for age-related macular degeneration: integrating segmentation and feature extraction for precise diagnosis.Scientific reports · 2026Article
- Predictive Cellular Signatures from Live Human Motor Neurons Distinguish TDP-43 ALS and Enable ALS Subtype Stratification.bioRxiv : the preprint server for biology · 2026Article
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- Toward accurate and scalable rainfall estimation using surveillance camera data and a hybrid deep-learning framework.Environmental science and ecotechnology · 2025Article
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- Enhancing active fire detection in Sentinel 2 imagery using GLCM texture features in random forest models.Scientific reports · 2024Article
- Computer Vision-Based Fire-Ice Ion Algorithm for Rapid and Nondestructive Authentication of Ziziphi Spinosae Semen and Its Counterfeits.Foods (Basel, Switzerland) · 2024Article
- Multi-type classification of lung nodules based on CT radiomics and ensemble learning for diversity weighting.Quantitative imaging in medicine and surgery · 2024Article
- Deep and shallow feature fusion framework for remote sensing open pit coal mine scene recognition.Scientific reports · 2024Article
- Accelerating whole-sample polarization-resolved second harmonic generation imaging in mammary gland tissue via generative adversarial networks.Biomedical optics express · 2024Article
- A non-invasive AI-based system for precise grading of anosmia in COVID-19 using neuroimaging.Heliyon · 2024Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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.
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