University of Tasmania
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Salad leaf disease detection using machine learning based hyper spectral sensing

Version 2 2025-01-15, 01:13
Version 1 2023-05-23, 18:40
conference contribution
posted on 2025-01-15, 01:13 authored by R Dutta, D Smith, Y Shu, Qing LiuQing Liu, P Doust, S Heidrich
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.

History

Publication title

Proceedings

Volume

60

Pagination

511-514

ISBN

978-1-4799-0160-9

Department/School

Information and Communication Technology

Publisher

IEEE

Publication status

  • Published

Place of publication

445 Hoes Lane Piscataway, NJ 08855-1331 United States

Event title

IEEE Sensors 2014

Event Venue

Valencia, Spain

Date of Event (Start Date)

2014-11-02

Date of Event (End Date)

2014-11-05

Socio-economic Objectives

229999 Other information and communication services not elsewhere classified