Speaker
Description
One of the most important parameters for the successful establishment of forest stands and forest habitat restoration programs is forest seedling quality. Some of the traditional methods for seedling quality assessment are invasive, time consuming, and labor-intensive. Hyperspectral imaging, in contrast, provides a noninvasive evaluation method, making it a new and a promising tool for seedling quality assessment. This is crucial from aspect of practical nursery production and outplanting success, but also as an background for development of new scientific methods. By capturing hundreds of continuous spectral bands, HSI enables the detection of detailed plant traits. This review focuses on the application of HSI in assessing forest seedling quality for application in nurseries and after outplanting in the frame of restoration initiatives highlighting its strong potential for evaluating key parameters. However, HSI alone cannot capture dimensional/physical traits, such as shoot height, root mass, bud dormancy, and therefore is best used in combination with complementary technologies, including LiDAR, vegetation indices, and image-based methods. This review also examines current challenges in implementation of HSI in operational nursery settings, such as illumination position sensitivity, plant-specific traits, large data volume, and high equipment cost. By integrating hyperspectral plant phenotyping and nursery practices, this review identifies future directions for HSI implementation in forest seedling production, thereby supporting more efficient nursery settings.
| Keywords | hyperspectral; seedling; noninvasive; nursery |
|---|
