Name
Abstract | ||
|---|---|---|
A new multispectral vision sensor for floral pear bud detection prior to bloom is elaborated.A two-season field trial was conducted during three early phenological stages.A custom image analysis algorithm for floral bud object detection is presented.83% (first season) and 78% (second season) of all floral buds could be successfully detected.A low number of false detections was achieved. Thinning of pome and stone fruit involves the reduction of tree crop load in order to regulate fruit set and quality. As it is typically carried out through manual labor, thinning comprises a large part of a grower's production costs. Mechanized thinning has been shown to be a cost-effective alternative but the performance of existing thinning devices needs to be further improved by taking the variation in bearing capacity of the individual trees into account.In this work, a multispectral camera system is developed to detect the floral buds of pear (cv. Conference) during the growth stages prior to bloom. During a two-year field trial, the multispectral system was used to measure orchard scenes in six distinct optical wavebands under controlled illumination. These wavebands are situated in the visible and near infrared region of the spectrum and were selected based on hyperspectral laboratory measurements described in previous work.The recorded multispectral images were converted to a database containing the spatial-spectral signatures of the objects present in the orchard. Subsequently, canonical correlation analysis was applied to create a spectral discriminant model that detects pixels originating from floral buds. This model was then applied to the recorded data after which an image analysis algorithm was designed and optimized to predict the number of floral buds. In total, approximately 87% of the visible floral buds were detected correctly with a low false discovery rate ( |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.compag.2015.01.015 | Computers and Electronics in Agriculture |
Keywords | Field | DocType |
Multispectral sensing,Pear,Mechanical thinning,Feature recognition | Object detection,Orchard,PEAR,Thinning,Remote sensing,Multispectral image,Hyperspectral imaging,Pixel,Engineering,Field trial | Journal |
Volume | Issue | ISSN |
113 | C | 0168-1699 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Niels Wouters | 1 | 0 | 0.34 |
| Bart De Ketelaere | 2 | 13 | 4.45 |
| Tom Deckers | 3 | 0 | 0.34 |
| Josse De Baerdemaeker | 4 | 0 | 1.35 |
| Wouter Saeys | 5 | 78 | 11.04 |