Abstract:The basic principle of silviculture is the rational use of natural regeneration. The acceleration and equalisation of seed germination and an increase of the field seed germination ability are affected by seed scarification, which results in the destruction or weakening of the seed cover. Acorn scarification is performed manually, in the standing position, most often in adapted work stations, whose geometry is adjusted by the staff to their own anthropometric dimensions. An added value of acorn scarification consists in the ability to visually assess the health status of the cotyledons visible on the cross-section, making it possible to infer the potential use of a seed for sowing. However, due to the scope and duration of the activities involved, manual scarification is a process that is monotonous and physically as well as psychologically tiring for its performer. Automating of this process allows for effective replacement of human labour. The results obtained from the use of the vision system designed to determine the length and orientation of acorns may be considered satisfactory. The implementation of the seed orientation detection algorithm using the Harris detector was 90% accurate. Studies and analyses have shown that the process of acorn scarification has a positive effect on the later improvement of uniformity and acceleration of seedling emergence. In the case of seeds subjected to scarification, 83% of the acorns germinated within 4 to 6 weeks after sowing.

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Efforts to predict the germination ability of acorns using their shape, length, diameter and density are reported in the literature. These methods, however, are not efficient enough. As such, a visual assessment of the viability of seeds based on the appearance of cross-sections of seeds following their scarification is used. This procedure is more robust but demands significant effort from experienced employees over a short period of time. In this article an automated method of acorn scarification and assessment has been announced. This type of automation requires the specific setup of a machine vision system and application of image processing algorithms for evaluation of sections of seeds in order to predict their viability. In the stage of the analysis of pathological changes, it is important to point out image features that enable efficient classification of seeds in respect of viability. The article shows the results of the binary separation of seeds into two fractions (healthy or spoiled) using average components of regular red-green-blue and perception-based hue-saturation-value colour space. Analysis of accuracy of discrimination was performed on sections of 400 scarified acorns acquired using two various setups: machine vision camera under uncontrolled varying illumination and commodity high-resolution camera under controlled illumination. The accuracy of automatic classification has been compared with predictions completed by experienced professionals. It has been shown that both automatic and manual methods reach an accuracy level of 84%, assuming that the images of the sections are properly normalised. The achieved recognition ratio was higher when referenced to predictions provided by professionals. Results of discrimination by means of Bayes classifier have been also presented as a reference.

Research Highlights: Seed separation criteria and the optimal parameters of sorting devices were described. Background and Objectives: Seeds are often sorted into fractions which are sown separately to promote uniform seed germination and seedling emergence. Therefore, the aim of this study was to determine the correlations between the basic physical properties of European black pine (Pinus nigra J.F. Arnold subsp. nigra) seeds for the needs of planning seed sorting operations. Materials and Methods: Black pine seeds were divided into 5 batches representing individual parent trees, and the physical properties (terminal velocity, thickness, width, length, angle of external friction, mass) of each seed were determined. The measured geometric parameters and seed mass were used to calculate the respective indicators for each seed. The values of the analyzed parameters were used to plan the seed separation process. Results: The average values of the basic physical properties of seeds were determined in the following range: Terminal velocity—8.32 to 8.73 m s−1, thickness—2.24 to 2.27 mm, width—3.34 to 3.44 mm, length—5.87 to 6.08 mm, angle of external friction—28 to 32°, mass—18.8 to 20.0 mg. Seed mass was most highly correlated with terminal velocity, and it was least correlated with the angle of external friction. Conclusions: The results of this study indicate that black pine seeds should be sorted with the use of pneumatic separators or, alternatively, mesh sieves with longitudinal openings. These sorting devices separate seeds into fractions characterized by similar seed mass, which delivers both economic and environmental benefits in nursery practice.

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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.

The use of modern multi-functional forestry machines has already been associated with central nervous system fatigue induced by high mental workload. As these machines are being used under increasingly difficult terrain conditions, further knowledge is required on the expected aggravation of operators’ mental workload, so that suitable work/rest schedules can be developed. Within such a context, the aim of this study was to gauge aggravations of mental workload derived from increasing slope gradient. Measurements of eye activity were obtained from a representative harvester operator working in corridors with the following mean inclinations: 9%, 23% and 47%. The duration, frequency and trajectory of eye movements were used to determine the harvester operator’s mental workload, on the assumption that worsening work conditions would be reflected by increased eyeball activity. The number of fixations during the performance of all tasks increased with the increasing slope gradient. Similarly, fixation duration increased with slope gradient. The mean duration of saccades when working on a 23% slope was 5% shorter compared to work under a 9% gradient. A further significant shortening of saccade duration (~22%) occurred when working on a 47% slope. The good match between eye activity cycles and work cycles, visible especially on steep slopes, indicates that mental workload is related to work conditions. Overall, operating a forest harvester on steep slopes results in a greatly increased mental workload and calls for suitable rest schedules.

Scarification involves the partial removal of the seed coat on the side of the hilum, opposite the radicle, to speed up germination in acorns. The aim of this study was to determine the influence of scarification on the germination capacity of pedunculate oak acorns, selected and prepared for sowing. The diameter, length and mass of acorns were measured before and after scarification in four batches of acorns harvested from uneven-aged trees (76, 91, 131 and 161 years). The measured parameters were used to determine the correlations between acorn dimensions and mass, and to calculate the dimensional scarification index and the mass scarification index in acorns. Individual complete and scarified acorns from every batch were germinated on sand and peat substrate for 28 days. The analyzed acorns were characterized by average size and mass. Scarification decreased acorn mass by around 22% and acorn length by around 31% on average. Scarification and the elimination of infected acorns increased germination capacity from around 64% to around 81% on average. Acorns can be divided into size groups before scarification to obtain seed material with varied germination capacity. Larger acorns with higher germination capacity can be used for sowing in container nurseries, whereas smaller acorns with lower germination capacity can be sown in open-field nurseries.