Assessment of Selected Parameters of the Automatic Scarification Device as an Example of a Device for Sustainable Forest Management

Abstract

Due to technological progress in forestry, seedlings with covered root systems-especially those grown in container nurseries-have become increasingly important in forest nursery production. One the trees that is most commonly grown this way is the common oak (Quercus robur L.). For an acorn to be sown in a container, it is necessary to remove its upper part during mechanical scarification, and evaluate its sowing suitability. At present, this is mainly done manually and by visual assessment. The low effectiveness of this method of acorn preparation has encouraged a search for unconventional solutions. One of them is the use of an automated device that consists of a computer vision-based module. For economic reasons related to the cost of growing seedlings in container nurseries, it is beneficial to minimize the contribution of unhealthy seeds. The maximum accuracy, which is understood as the number of correct seed diagnoses relative to the total number of seeds being assessed, was adopted as a criterion for choosing a separation threshold. According to the method proposed, the intensity and red components of the images of scarified acorns facilitated the best results in terms of the materials examined during the experiment. On average, a 10% inaccuracy of separation was observed. A secondary outcome of the presented research is an evaluation of the ergonomic parameters of the user interface that is attached to the unit controlling the device when it is running in its autonomous operation mode.

Influence of Scarification on the Germination Capacity of Acorns Harvested from Uneven-Aged Stands of Pedunculate Oak (Quercus robur L.)

“…Unlike the seeds of other forest trees [43], pedunculate oak acorns cannot be sorted effectively based on physical parameters; therefore, the optical parameters of acorn cross-sections could be used as an innovative selection trait. Optical parameters cannot be reliably evaluated by the naked eye, which is why an automated scarification device with a vision system has been developed [8,24] to identify early symptoms of disease, eliminate damaged acorns and increase germination capacity by up to 10% relative to manually processed material. However, evaluations of acorn health can be compromised by two types of errors.…”

Automation of the Acorn Scarification Process as a Contribution to Sustainable Forest Management. Case Study: Common Oak

“…Previous practical experiments show that the effectiveness of such visual recognition of oak seed germination ability is over 80% [23,24]. …”

“…Only a light-coloured, single-colour seed promises a chance of proper growth of a seedling; dark cotyledons show fungus disease and such a seeds are rejected. Previous practical experiments show that the effectiveness of such visual recognition of oak seed germination ability is over 80% [23,24]. At a distance of about 1/5 of the length of the seed, it is cut using pruning shears and then visually evaluated for its suitability for planting.…”

Influence of Scarification on the Germination Capacity of Acorns Harvested from Uneven-aged Stands of Pedunculate Oak (<em>Quercus robur </em>L.)

“…Unlike the seeds of other forest trees [43], pedunculate oak acorns cannot be sorted effectively based on physical parameters; therefore, the optical parameters of acorn cross-sections could be used as an innovative selection trait. Optical parameters cannot be reliably evaluated with a naked eye, which is why an automated scarification device with a vision system has been developed [8,24] to identify early symptoms of disease, eliminate damaged acorns and increase germination capacity by up to 10% relative to manually processed material. However, evaluations of acorn health can be compromised by two types of errors.…”

“…Mummified acorns are eliminated at this stage [8,19]. Researchers are currently designing a robot system that will eliminate manual sorting, increase scarification efficiency and maximize the percentage of healthy acorns in the sorted batch [8,[20][21][22][23][24]. Automated scarification will be a highly accurate process, and the removed portion of the acorn will be minimized to guarantee the highest germination capacity.…”