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Machine learning for diagnosis of disease in plants using spectral data

 
: Owomugisha, G.; Melchert, F.; Mwebaze, E.; Quinn, J.A.; Biehl, M.

Arabnia, H.R. ; Computer Science, Research, Education & Applications -CSREA-:
ICAI '18. Proceedings of the 2018 International Conference on Artificial Intelligence : Publication of the 2018 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE '18), July 30-August 02, 2018, Las Vegas, Nevada, USA
Las Vegas: CSREA Press, 2018 (2018 World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2018 - Proceedings of the 2018 International Conference on Artificial Intelligence, ICAI 2018)
ISBN: 1-60132-480-4
ISBN: 978-1-60132-480-1
S.9-15
International Conference on Artificial Intelligence (ICAI) <2018, Las Vegas/Nev.>
World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE) <2018, Las Vegas/Nev.>
Englisch
Konferenzbeitrag
Fraunhofer IFF ()

Abstract
Automating crop disease diagnosis is an important task, particularly for regions with few experts. Most current methods detect disease by analyzing leaf images, particularly for diseases that manifest on the aerial part of the plant. To train a good classifier one requires a huge image dataset and the appropriate methods to extract relevant features from the images that represent the disease unambiguously. Image data also tends to be prone to effects of occlusion that make consistent analysis of the data hard. In this paper we take a look at the use of spectral data collected from leaves of a plant. We analyse spectral data from visibly diseased parts of a leaf as well as parts that are visibly healthy. We employ prototype based classification methods and standard classification models in a three-class classification problem configuration. Results presented show significant improvement in performance when spectral data is used and the possibility of early detection of disease before the crops become visibly symptomatic, which for practical reasons is very important.

: http://publica.fraunhofer.de/dokumente/N-581687.html