eprintid: 17457
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/74/57
datestamp: 2023-12-19 03:23:50
lastmod: 2023-12-19 03:23:50
status_changed: 2023-12-19 03:08:05
type: conference_item
metadata_visibility: show
creators_name: Abdul Rahman, A.S.B.
creators_name: Izhar, L.I.
creators_name: Sebastian, P.
creators_name: Rohmah, R.N.
title: Multispectral Image Analysis for Crop Health Monitoring System
ispublished: pub
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
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
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. © 2022 IEEE.
date: 2022
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85141575406&doi=10.1109%2fROMA55875.2022.9915668&partnerID=40&md5=4ba0097ecc79d16845d2143da0360237
id_number: 10.1109/ROMA55875.2022.9915668
full_text_status: none
publication: 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation, ROMA 2022
refereed: TRUE
isbn: 9781665459327
citation:   Abdul Rahman, A.S.B. and Izhar, L.I. and Sebastian, P. and Rohmah, R.N.  (2022) Multispectral Image Analysis for Crop Health Monitoring System.  In: UNSPECIFIED.