1. |
- Berglund, Anders, et al.
(author)
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Improved log rotation using information from a computed tomography scanner
- 2013
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In: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 90, s. 152-158
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Journal article (peer-reviewed)abstract
- The development of an industrial computed tomography scanner for the sawmilling industry raises the question of how to find a production strategy that uses a computed tomography scanner in the sawmill production line to its full potential. This study was focused on a Scandinavian sawmill processing Scots pine (Pinus sylvestris) and Norway spruce (Picea abies). The potential value increase when allowing an alternative log rotation other than the horns down position was investigated using a log breakdown simulation. The resulting data was analysed with respect to the size of the log rotational step, an introduced rotational error of the sawing machine and different price differences between the quality grades. It was also of interest to define the outer log properties that characterise the logs sawn for the greatest profit return close to the horns down position compared to logs sawn for a greater profit return in a different log rotation. Such characteristics can be used to reduce the number of degrees of freedom in an optimisation and consider instead other parameters, such as positioning and sawing pattern. Other defects such as pitch pockets, splits and rot are also of interest. The results shows that there is a potential value increase when applying the log rotation that maximises the value for each log instead of processing all logs in the horns down position. However, the potential value increase depends on the rotational error of the used sawing machine and the price differences between the quality grades. The log properties that differ between logs sawn for the greatest profit return close to the horns down position compared to a different log rotation are the bow height and the log taper. Unfortunately, predictability of log rotation for greatest profit return based on the outer properties of logs is poor. It is not possible to differentiate logs which would be sawn for the greatest profit return close to the horns down position from those where a different log rotation results in the greatest profit return, based only on their outer properties.
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2. |
- Bolinder, Martin
(author)
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A neural network experiment on the site-specific simulation of potato tuber growth in Eastern Canada
- 2010
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In: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 73, s. 126-132
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Journal article (peer-reviewed)abstract
- The objective of this work was to optimize a neural network (NN) for modelling potato tuber growth and its in-field variations in eastern Canada In addition to climatic inputs, the cumulative and maximal leaf area index (LAI) were incorporated to account for in-field scale variability Soil and genetic parameters were assumed to be integrated in LAI as suggested by earlier work. Each input and combination of inputs was evaluated from the changes they induced in MAE (mean absolute error) and RMSE (root mean square error). Results using data from several replicated on-farm experiments between 2005 and 2008 suggest that a NN model using cumulative solar radiation, cumulative rainfall and cumulative LAI can adequately model site-specific tuber growth. The MAE of the retained model was 209 kg DM ha(-1), which represents less than 4% of the mean final tuber yield for the 3 years of the study Non-linear effects of explicative variables on tuber yield were attested by comparing the results of the NN simulations to those of a multiple linear regression (MLR). The failure of MLR to simulate temporal discontinuities in tuber growth supports the use of a non-linear approach such as a NN to model tuber growth (C) 2010 Elsevier B V All rights reserved.
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3. |
- Johansson, Erik, et al.
(author)
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Automated knot detection for high speed computed tomography on Pinus sylvestris L. and Picea abies (L.) Karst. using ellipse fitting in concentric surfaces
- 2013
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In: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 96, s. 238-245
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Journal article (peer-reviewed)abstract
- High speed industrial computed tomography (CT) scanning of sawlogs is new to the sawmill industry and therefore there are no properly evaluated algorithms for detecting knots in such images. This article presents an algorithm that detects knots in CT images of logs by segmenting the knots with variable thresholds on cylindrical shells of the CT images. The knots are fitted to ellipses and matched between several cylindrical shells. Parameterized knots are constructed using regression models from the matched knot ellipses. The algorithm was tested on a variety of Scandinavian Scots pine (. Pinus sylvestris L.) and Norway spruce (. Picea abies (L.) Karst.) with a knot detection rate of 88-94% and generating about 1% falsely detected knots.
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5. |
- Norell, Kristin
(author)
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Automatic counting of annual rings on Pinus sylvestris end faces in sawmill industry
- 2011
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In: Computers and Electronics in Agriculture. - : Elsevier BV. - 0168-1699 .- 1872-7107. ; 75, s. 231-237
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Journal article (peer-reviewed)abstract
- The quality of wood can be analyzed using annual ring width. At Swedish sawmills, this is performed using manual inspection for grading purpose. Here a completely automatic method for counting the number of annual rings on log end faces is described and evaluated. The method is applied and tested using images from Pinus sylvestris end faces acquired in online sawmill production with a camera mounted above a conveyor belt. Completely untreated end faces were captured, as well as newly sawn ones. The proposed method includes preprocessing, pith detection and counting the number of rings in two regions of the end face. A new method to remove marks from uneven sawing is presented as a part of the preprocessing steps. The evaluation shows that the suggested automatic method performs as well as the manual measurements that are the method used for measuring today. (C) 2010 Elsevier B.V. All rights reserved.
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