Salad leaf disease detection using machine learning based hyper spectral sensing
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conference contribution
posted on 2025-01-15, 01:13authored byR 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