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.
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.
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 objective of the paper was to determine the work expenditures and costs of eradication of an energy willow plantation with currently applied mechanical methods and with the use of the test model of a machine for cutting willow rootstocks as a part of the scientific project no. PBS2/A8/26/2014. The scope of the paper covered research for four machine units constructed for a twelve-year willow plantation with the surface area of 3 ha. Work inputs for eradication of the plantation of the investigated aggregates were within 8.1 to 50.4 mhr•ha -1 . Work inputs with the new machine were 22.3 mhr•ha -1 . The level of work inputs was influenced by low working speeds of the tractor-machine unit and working speeds from 0.4 to 2.3 m. Costs of willow plantation eradication with current mechanical methods were from 4302 to 15536 PLN•ha -1 , and with the use of the new machine it was 5457 PLN•ha -1 .
The goal of the research described in the article was to develop the device for the automatic scarification of acorns and computer vision-based assessment of their viability. The color image of the intersection of the tissue of cotyledons was selected as a key feature for separating healthy seeds from the spoiled ones. Because the device is being designed for the diagnosis of high volume of seeds aiming at producing high-quality seedlings, several assessment criteria of the overall design of the automaton are being assessed. The basic one is the overall accuracy of viability recognition. The other refers to particular functions implemented in the model of the device being described.
Forest regeneration by means of seedlings grown in container nurseries is usually performed manually with the use of the standard dibble bar or the tube dibble. Manual placement of a large number of seedlings in the soil requires a lot of work. Manual removal of the soil cover and digging the soil in spots with a diameter of 0.4 m requires, under average conditions, about 38 man-hours/ha, while planting with a dibble bar requires about 34 man-hours/ha. Additional work time is needed to carry seedlings over an area that is being afforested. At present, forestry does not have automatic planters that would enable the establishment of forest cultures. The aim of the paper is to present the concept of an autonomous robot and an innovative technology of performing forest regeneration and afforestation of former agricultural and reclaimed areas. The paper also presents the design solutions of the key working unit, which is a universal, openable dibble, cooperating with a three-toothed shaft to prepare a planting spot. The solution proposed enables continuous operation of the machine, i.e. without the need to stop the base vehicle.
The objective of the paper was to determine fuel consumption on elimination of the energy willow plantation with current mechanical methods with the use of the machine research model. The paper covers investigations of four machine units. The lowest fuel consumption (142.6 l•ha -1 ) with the use of Meri Crusher MJS-2.0) did not ensure effectiveness of operation of this unit. Efficiency of elimination of the plantation in this case is only 36.4%. On the other hand, the highest consumption of diesel oil (776.4 l•ha -1 ) was reported for FAO FAR model FV 4088, and the effectiveness of elimination was not satisfactory and it was 57.0%. The highest effectiveness of elimination of the plantation was reported for the model of a new machine. Fuel consumption in this case was 535.7 l•ha -1 and the willow plantation elimination effectiveness was the highest and it amounted to 94.8%.
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