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Salad leaf disease detection using machine learning based hyper spectral sensing

Citation

Dutta, R and Smith, D and Shu, Y and Liu, Q and Doust, P and Heidrich, S, Salad leaf disease detection using machine learning based hyper spectral sensing, Proceedings, 2-5 November 2014, Valencia, Spain, pp. 511-514. ISBN 978-1-4799-0160-9 (2014) [Non Refereed Conference Paper]


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DOI: doi:10.1109/ICSENS.2014.6985047

Abstract

In this paper a novel application of salad leaf disease detection has been developed using a combination of machine learning algorithms and Hyper Spectral sensing. Various field experiments were conducted to acquire different vegetation reflectance spectrum profiles using a portable high resolution ASD FieldSpec4 Spectroradiometer, at a farm located in Richmond, Tasmania, Australia, (-42.36, 147.29), A total of 105 spectral samples were collected through three different experiments with baby salad leaves. In this study, Principal Component Analysis (PCA), Multi-Statistics Feature ranking and Linear Discriminant Analysis (LDA) Classifiers were used to classify disease affected salad leaves from the healthy salad leaves with 84% classification accuracy. This study concluded that the machine learning based approach along with a high resolution hyper Spectroradiometer could potentially provide a novel mechanism to use in the farm for rapid detection of salad leaf disease.

Item Details

Item Type:Non Refereed Conference Paper
Keywords:salad, leaf disease, disease detection, machine learning, hyper-spectral sensing
Research Division:Agricultural and Veterinary Sciences
Research Group:Crop and Pasture Production
Research Field:Crop and Pasture Protection (Pests, Diseases and Weeds)
Objective Division:Information and Communication Services
Objective Group:Other Information and Communication Services
Objective Field:Information and Communication Services not elsewhere classified
Author:Liu, Q (Dr Qing Liu)
ID Code:117363
Year Published:2014
Deposited By:Information and Communication Technology
Deposited On:2017-06-08
Last Modified:2017-06-08
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