Early multispectral detection of floral pear buds for automated thin- ning
نویسندگان
چکیده
Thinning of fruit trees is a commonly used technique for improving yield regularity and fruit quality. Since few effective chemical thinning agents are available, thinning is still mostly carried out through expensive manual labor. Thinning machines developed in recent years have demonstrated that automated thinning can be a viable alternative for the traditional methods and can yield economic savings. Despite this, existing devices generally still lack sufficient selectivity to deal with the non-uniform distribution of floral buds throughout an orchard. Online knowledge of the location of the floral buds can significantly improve the thinning performance of these machines. Additionally, with this information, unnecessary tree damage can be prevented and the subsequent risk of disease spread reduced. In this work we present a multispectral vision sensor to detect floral pear buds (cultivar Conference) during the phenological stadia occurring before bloom. To this end, a camera system with a resolution of 1040 x 1392 pixels was equipped with six optical bandpass filters in the range 400-1000 nm that could be sequentially rotated in front of the lens. The transmission bands of the filters were selected based on previous work, in which hyperspectral measurements were performed to determine the optimal wavebands for discriminating between floral pear buds and their environment. The multispectral sensor was used to conduct nighttime measurements in the orchard during two flowering seasons. Controlled illumination of each scene was provided through a halogen lamp. Subsequently, the resulting images were used to build a spatial-spectral database for each type of object occurring in the orchard. The spectral information was then used to build a discriminant model through canonical correlation analysis. Finally, this model was applied to the recorded data after which image analysis was employed to predict the number of floral buds. The results showed that under good illumination conditions more than 87 % of the unoccluded floral buds in the foreground can be detected correctly with a low false classification rate (14 %).
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