@inproceedings{scholars17457,
            note = {cited By 0; Conference of 5th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2022 ; Conference Date: 6 August 0202 Through 8 August 0202; Conference Code:183507},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
             doi = {10.1109/ROMA55875.2022.9915668},
         journal = {2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022},
           title = {Multispectral Image Analysis for Crop Health Monitoring System},
            year = {2022},
        keywords = {Decision trees; Machine learning; Random forests; Vegetation mapping, Crop health assessment; Health assessments; Health monitoring system; Machine-learning; Multi-spectral; Multi-spectral image analysis; Multispectral images; Normalized difference vegetation index; Potato crop; Random forest classifier, Crops},
        abstract = {The goal of this research is to apply machine learning to classify healthy and unhealthy potato crops collected from UAV-based multispectral images, and to establish which spectral band provides the best separation for classification. Traditional detection and mapping approaches take time, involve a lot of human work, and are often subjective. The classification will use the Random Forest Classifier as the machine learning technique to classify based on two vegetation indices: the Normalized Difference Vegetation Index (NDVI) and the Red Edge Normalized Difference Vegetation Index (NDRE). The proposed method includes three primary components: (1) raw picture radiometric correction and orthomosaic combination; (2) dirt and weed removal using a thresholding method; and (3) classification and model training using Random Forest Classifier. The method's performance is assessed using data from an experimental potato field published by the University of Idaho. {\^A}{\copyright} 2022 IEEE.},
            isbn = {9781665459327},
          author = {Abdul Rahman, A. S. B. and Izhar, L. I. and Sebastian, P. and Rohmah, R. N.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141575406&doi=10.1109\%2fROMA55875.2022.9915668&partnerID=40&md5=4ba0097ecc79d16845d2143da0360237}
}