A survey of the skidder fleet of Central, Eastern and Southern Europe

Spinelli, Raffaele; Magagnotti, Natascia; Visser, Rien; O'Neal, Brandon

doi:10.1007/s10342-021-01374-z

Cited by 19 publications

(12 citation statements)

References 10 publications

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“…At this level, manual-dominated tasks have been approached in forestry under the umbrella of the so-called human activity recognition, which has been implemented by the use of various data collection platforms and machine learning techniques e.g., [ 11 , 12 ]. A similar approach has been used to monitor tool or machine-supported tasks, at least when such machines were not equipped with built-in production monitoring systems [ 8 , 9 , 10 , 13 ]; this approach still justifies its relevance due to the low to intermediary mechanization level of forest operations that still prevails in many parts of the world [ 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Improving the Event-Based Classification Accuracy in Pit-Drilling Operations: An Application by Neural Networks and Median Filtering of the Acceleration Input Signal Data

Pérez

Borz

2021

Sensors

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Forestry is a complex economic sector which is relying on resource and process monitoring data. Most of the forest operations such as planting and harvesting are supported by the use of tools and machines, and their monitoring has been traditionally done by the use of pen-and-paper time studies. Nevertheless, modern data collection and analysis methods involving different kinds of platforms and machine learning techniques have been studied lately with the aim of easing the data management process. By their outcomes, improvements are still needed to reach a close to 100% activity recognition, which may depend on several factors such as the type of monitored process and the characteristics of the signals used as inputs. In this paper, we test, thought a case study on mechanized pit-drilling operations, the potential of digital signal processing techniques combined with Artificial Neural Networks (ANNs) in improving the event-based classification accuracy in the time domain. Signal processing was implemented by the means of median filtering of triaxial accelerometer data (window sizes of 3, 5, and up to 21 observations collected at 1 Hz) while the ANNs were subjected to the regularization hyperparameter’s tunning. An acceleration signal processed by a median filter with a window size of 3 observations and fed into an ANN set to learn and generalize by a regularization parameter of α = 0.01 has been found to be the best strategy in improving the event-based classification accuracy (improvements of 1% to 8% in classification accuracy depending on the type of event in question). Improvement of classification accuracy by signal filtering and ANN tuning may depend largely on the type of monitored process and its outcomes in terms of event duration; therefore, other monitoring applications may need particular designs of signal processing and ANN tuning.

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Section: Introductionmentioning

confidence: 99%

Improving the Event-Based Classification Accuracy in Pit-Drilling Operations: An Application by Neural Networks and Median Filtering of the Acceleration Input Signal Data

Pérez

Borz

2021

Sensors

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“…In SW Europe, the deployment of harvesters is often not possible due to difficult terrain [66], legislation or limited financial capacity [67]. New winch-assisted technology may soon provide a technical solution to the terrain limitation; nevertheless, deep structural changes in the legislative and economic environment would be necessary to promote harvester adoption and use [68].…”

Section: South-west Europementioning

confidence: 99%

Mechanised Harvesting of Broadleaved Tree Species in Europe

et al. 2022

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Purpose of the Review The application of cut-to-length (CTL) technology for harvesting and processing timber from broadleaved tree species is a challenging process, due to thick branches, stem sweep, forks and high wood density. The objective of this review was to describe the current state of harvester use and characterise the effectiveness of mechanised forest operations for broadleaved and mixed forest stands in Europe, paying particular attention to productivity and product quality aspects. An additional aim was to identify the greatest difficulties associated with harvesting broadleaved tree species today and to indicate future challenges and areas for improvement. Recent Findings An interest in wider harvester use for broadleaved tree species started after the storm Lothar in France in 1999. Early experiences demonstrated that lower productivity is common when harvesting broadleaved tree species, due to limitations in delimbing and the high frequency of sweep. Initially, modifications were made to harvester heads, while a newly designed head (CTL 40 HW) was later developed within a European project in 2005–2007. Using various heads, very high productivity was achieved in some cases, but log recovery was unsatisfactory, with large tree tops left without delimbing. The delimbing was of lower quality and log length inaccuracy was observed, as well as economic losses due to damage to plywood caused by feed rollers. These issues resulted in a change in harvester head construction, including work on a patent for a head designed specifically for harvesting broadleaved tree species. Summary The harvester heads that have been developed to date for broadleaved tree species have mainly been used within Central Europe, where the high incidence of broadleaved tree species and their particular morphological features (thick branches and sweep) are a major challenge for CTL technology. There have been many research projects related to this issue: in the last 15 years, there were a few larger initiatives leading to the design and development of harvester heads for broadleaved tree species, with participation from institutions and universities in France, Germany and Poland. There are currently no ongoing initiatives, but the formation of new ones is strongly recommended because today’s forests have a growing share of broadleaved tree species and are being managed to an increasing extent with CTL technology.

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“…Based on the engine power classification proposed by Spinelli et al [48], the skidder was a medium-sized vehicle (power 110 kW) while the forwarder (Figure 1) belonged to a medium-class loading capacity (from 10 t to 14 t), according to Brunberg [49]. A forest work time study was conducted according to nomenclature proposed by Björheden et al [50] and Acuna et al [51].…”

Section: Logging Scenario Organisation and Technical Performances Eva...mentioning

confidence: 99%

Assessment and Sustainability of Logging Operations in Calabrian Pine High Forests

Bernardi

Macrì

Falcone

et al. 2022

Forests

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Forest mechanisation plays an important role in increasing labour productivity and reducing production costs. This work aims at evaluating various logging scenarios in Calabrian pine high forests, considering technical, economic and environmental aspects. The cut-to-length system was adopted and structured as follows: felling and processing operations were carried out using a medium-sized chainsaw while extraction of the processed material was carried out using three different vehicles for timber extraction: (i) by cable skidder, (ii) by grapple skidder and (iii) by a forwarder. The methodology was based on productivity analysis and production cost analysis, while for environmental performance, the life cycle assessment (LCA) approach was adopted. The selected functional unit (FU) was referred to as 1 h of logging operations. However, to assess the resulting usefulness, further analyses were performed using an alternative FU consisting of 1 m3 of round wood. The study’s outcomes show the complexity in achieving an optimal balance between productivity, economic aspects and sustainable management in forest operations.

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A survey of the skidder fleet of Central, Eastern and Southern Europe

Cited by 19 publications

References 10 publications

Improving the Event-Based Classification Accuracy in Pit-Drilling Operations: An Application by Neural Networks and Median Filtering of the Acceleration Input Signal Data

Improving the Event-Based Classification Accuracy in Pit-Drilling Operations: An Application by Neural Networks and Median Filtering of the Acceleration Input Signal Data

Mechanised Harvesting of Broadleaved Tree Species in Europe

Assessment and Sustainability of Logging Operations in Calabrian Pine High Forests

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