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

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

Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, Al. A. Mickiewicza 30, Kraków 30-059, Poland
Faculty of Forestry, University of Agriculture in Krakow, UR, Al. 29 Listopada 46, Kraków 31-425, Poland
Industrial Institute of Agricultural Engineering, Przemysłowy Instytut Maszyn Rolniczych, PIMR, ul. Starołęcka 31, Poznan 60-963, Poland
Faculty of Production and Power Engineering, University of Agriculture in Krakow, UR, ul. Balicka 116B, Kraków 30-149, Poland
Faculty of Technical Sciences, University of Warmia and Mazury in Olsztyn, UWM, ul. Oczapowskiego 11, Olsztyn 10-719, Poland
Author to whom correspondence should be addressed.
Sustainability 2017, 9(12), 2370;
Received: 7 November 2017 / Revised: 15 December 2017 / Accepted: 16 December 2017 / Published: 19 December 2017
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. View Full-Text
Keywords: acorn; scarification; quality assessment; computer vision system; sowing material acorn; scarification; quality assessment; computer vision system; sowing material
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Tadeusiewicz, R.; Tylek, P.; Adamczyk, F.; Kiełbasa, P.; Jabłoński, M.; Bubliński, Z.; Grabska-Chrząstowska, J.; Kaliniewicz, Z.; Walczyk, J.; Szczepaniak, J.; Juliszewski, T.; Szaroleta, M. Assessment of Selected Parameters of the Automatic Scarification Device as an Example of a Device for Sustainable Forest Management. Sustainability 2017, 9, 2370.

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