Sensors 2018, 18(9), 2770; https://doi.org/10.3390/s18092770
Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination
Afroditi Alexandra Tamouridou 1, Xanthoula Eirini Pantazi 1
, Thomas Alexandridis 2, Anastasia Lagopodi 3, Giorgos Kontouris 2 and Dimitrios Moshou 1,*
, Thomas Alexandridis 2, Anastasia Lagopodi 3, Giorgos Kontouris 2 and Dimitrios Moshou 1,*
1
Agricultural Engineering Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
2
Laboratory of Remote Sensing and GIS, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
3
Plant Pathology Laboratory, Faculty of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Received: 26 July 2018 / Revised: 10 August 2018 / Accepted: 20 August 2018 / Published: 23 August 2018
(This article belongs to the Special Issue Sensors in Agriculture 2018)
Abstract
Microbotryum silybum, a smut fungus, is studied as an agent for the biological control of Silybum marianum (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Nonetheless, in situ diagnosis is challenging. The presently demonstrated research illustrates the identification process of systemically infected S. marianum plants by means of field spectroscopy and the multilayer perceptron/automatic relevance determination (MLP-ARD) network. Leaf spectral signatures were obtained from both healthy and infected S. marianum plants using a portable visible and near-infrared spectrometer (310–1100 nm). The MLP-ARD algorithm was applied for the recognition of the infected S. marianum plants. Pre-processed spectral signatures served as input features. The spectra pre-processing consisted of normalization, and second derivative and principal component extraction. MLP-ARD reached a high overall accuracy (90.32%) in the identification process. The research results establish the capacity of MLP-ARD to precisely identify systemically infected S. marianum weeds during their vegetative growth stage. View Full-TextKeywords:
plant pathology; MLP-ARD; disease detection; artificial intelligence; precision agriculture
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Tamouridou, A.A.; Pantazi, X.E.; Alexandridis, T.; Lagopodi, A.; Kontouris, G.; Moshou, D. Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination. Sensors 2018, 18, 2770.
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