Hyperspectral waveband selection for automatic detection of floral pear buds

Hyperspectral waveband selection for automatic detection of floral pear buds Thinning of fruit-tree blossoms is used to regulate the yearly tree bearing and to increase the fruit yield and quality. While this is still mostly done by hand, the increasing costs of manual labor have created a demand for mechanization. This has recently led to the development of several prototype thinning machines. The main disadvantage of these machines is that they are not selective, while the fruit bearing capacity of different floral buds is not equal. On-line information about the position and distribution of the floral buds on the tree can improve the efficiency of mechanized thinning. Therefore, the aim of this study was to identify the most informative wavebands to develop a multispectral vision sensor for detection of the floral buds of the pear cultivar Conference. Hyperspectral scans were taken from tree samples in five early phenological stages to create a database of reflectance spectra for the different tree features. A stepwise algorithm was then applied to this training set to select the best combination of wavebands having the highest discriminating power between the components of interest. Subsequently, canonical correlation analysis was used to create discriminant functions out of the selected wavebands. It was possible to correctly classify 95 % of the (pixel) observations with six selected wavebands. The discrimination performance was also tested as a function of the number of used wavebands. Analysis showed that when only the two most important wavebands were used, still over 90 % of the (pixel) observations could be correctly classified. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Hyperspectral waveband selection for automatic detection of floral pear buds

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Springer US
Copyright © 2012 by Springer Science+Business Media, LLC
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Meteorology/Climatology
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