Detection of grapevine leafroll disease based on 11-index imagery and ant colony clustering algorithm

Detection of grapevine leafroll disease based on 11-index imagery and ant colony clustering... Grapevine leafroll disease (GLD) is a virus disease that quickly propagates through vineyards under appropriate weather conditions and can reduce grape production worldwide by nearly 60 %. Therefore, the accurate diagnosis and reliable evaluation of GLD distribution, particularly at the early stage of GLD infection, is important to prevent the spread of this disease. This study applied the ant colony clustering algorithm (ACCA) to detect GLD spectral anomalies on 4 GLD-infected vineyards according to multi-spectral imagery for precision disease management. GLD was classified into three stages: GLD1, GLD2 and GLD3 according to its infection severity. An 11-index feature vector and its stacked image were generated to enhance the spectral differences and spectral discrimination between diseased and healthy grapevines. ACCA was then designed to solve the fuzziness of the multi-spectral image for GLD-infected grapevines and successfully identify GLD from healthy grapevines. Finally, a field survey with 49 samples and pixel purity index technology were applied to validate the effectiveness, efficiency and accuracy of ACCA. Field results indicated that an early stage of the GLD infection (GLD1) could be successfully discriminated from GLD2-, GLD3- and non-infected grapevines. The classification accuracies of non-, GLD1-, GLD2- and GLD3-infected grapevines were 94.4, 75, 84.6 and 83.3 %, respectively. Hence, the method based on an 11-index image and ACCA may significantly detect GLD at an early stage from healthy grapevines for precision disease management at the field level. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Detection of grapevine leafroll disease based on 11-index imagery and ant colony clustering algorithm

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Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-016-9432-2
Publisher site
See Article on Publisher Site

Abstract

Grapevine leafroll disease (GLD) is a virus disease that quickly propagates through vineyards under appropriate weather conditions and can reduce grape production worldwide by nearly 60 %. Therefore, the accurate diagnosis and reliable evaluation of GLD distribution, particularly at the early stage of GLD infection, is important to prevent the spread of this disease. This study applied the ant colony clustering algorithm (ACCA) to detect GLD spectral anomalies on 4 GLD-infected vineyards according to multi-spectral imagery for precision disease management. GLD was classified into three stages: GLD1, GLD2 and GLD3 according to its infection severity. An 11-index feature vector and its stacked image were generated to enhance the spectral differences and spectral discrimination between diseased and healthy grapevines. ACCA was then designed to solve the fuzziness of the multi-spectral image for GLD-infected grapevines and successfully identify GLD from healthy grapevines. Finally, a field survey with 49 samples and pixel purity index technology were applied to validate the effectiveness, efficiency and accuracy of ACCA. Field results indicated that an early stage of the GLD infection (GLD1) could be successfully discriminated from GLD2-, GLD3- and non-infected grapevines. The classification accuracies of non-, GLD1-, GLD2- and GLD3-infected grapevines were 94.4, 75, 84.6 and 83.3 %, respectively. Hence, the method based on an 11-index image and ACCA may significantly detect GLD at an early stage from healthy grapevines for precision disease management at the field level.

Journal

Precision AgricultureSpringer Journals

Published: Jan 25, 2016

References

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