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Detection of white leaf disease in sugarcane using machine learning techniques over UAV multispectral images

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dc.contributor.author Narmilan, Amarasingam
dc.contributor.author Felipe, Gonzalez
dc.contributor.author Surantha, Salgadoe
dc.contributor.author Kevin, Powell
dc.date.accessioned 2022-09-05T05:06:36Z
dc.date.available 2022-09-05T05:06:36Z
dc.date.issued 2022-09-01
dc.identifier.citation Drones; 6(9); pp:230 en_US
dc.identifier.issn 2504-446X
dc.identifier.uri https://doi.org/10.3390/drones6090230
dc.identifier.uri http://ir.lib.seu.ac.lk/handle/123456789/6244
dc.description.abstract Sugarcane white leaf phytoplasma (white leaf disease) in sugarcane crops is caused by a phytoplasma transmitted by leafhopper vectors. White leaf disease (WLD) occurs predominantly in some Asian countries and is a devastating global threat to sugarcane industries, especially Sri Lanka. Therefore, a feasible and an effective approach to precisely monitoring WLD infection is important, especially at the early pre-visual stage. This work presents the first approach on the preliminary detection of sugarcane WLD by using high-resolution multispectral sensors mounted on small unmanned aerial vehicles (UAVs) and supervised machine learning classifiers. The detection pipeline discussed in this paper was validated in a sugarcane field located in Gal-Oya Plantation, Hingurana, Sri Lanka. The pixelwise segmented samples were classified as ground, shadow, healthy plant, early symptom, and severe symptom. Four ML algorithms, namely XGBoost (XGB), random forest (RF), decision tree (DT), and K-nearest neighbors (KNN), were implemented along with different python libraries, vegetation indices (VIs), and five spectral bands to detect the WLD in the sugarcane field. The accuracy rate of 94% was attained in the XGB, RF, and KNN to detect WLD in the field. The top three vegetation indices (VIs) for separating healthy and infected sugarcane crops are modified soil-adjusted vegetation index (MSAVI), normalized difference vegetation index (NDVI), and excess green (ExG) in XGB, RF, and DT, while the best spectral band is red in XGB and RF and green in DT. The results revealed that this technology provides a dependable, more direct, cost-effective, and quick method for detecting WLD en_US
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.subject white leaf disease en_US
dc.subject precision agriculture en_US
dc.subject UAV multispectral images en_US
dc.subject machine learning en_US
dc.subject sugarcane en_US
dc.title Detection of white leaf disease in sugarcane using machine learning techniques over UAV multispectral images en_US
dc.type Article en_US


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  • Research Articles [915]
    THESE ARE RESEARCH ARTICLES OF ACADEMIC STAFF, PUBLISHED IN JOURNALS AND PROCEEDINGS ELSWHERE

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