%0 Conference Paper
%A Abdul Rahman, A.S.B.
%A Izhar, L.I.
%A Sebastian, P.
%A Rohmah, R.N.
%D 2022
%F scholars:17457
%I Institute of Electrical and Electronics Engineers Inc.
%K 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
%R 10.1109/ROMA55875.2022.9915668
%T Multispectral Image Analysis for Crop Health Monitoring System
%U https://khub.utp.edu.my/scholars/17457/
%X 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. © 2022 IEEE.
%Z 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