Using Machine Vision to Improve the Efficiency of Lumber Mills

Thinning treatment is a necessary and complex forestry activity. The experimental results from plantations established 20-30 years ago and explains some concepts of the theory, practice, methods, and regime of thinning on the permanent sample plots of pine stands in Gatchinsky forestry of the Leningrad region were shown in this article. Choosing the right thinning method allows to optimize the yield, productivity and mortality of the stand. On the other hand, we observed improved merchantability of the stand, reduced time for forestation, and simplified thinning programs. Crown thinning is a less preferred method than bottom thinning, as it leads to a deterioration in the quality of trees and an increase in their mortality. The inexpediency of preliminary thinning of trees has been established. Tree thinning results in an improvement in the quality of the remaining stand, as nutrient utilization is greatly increased. Tree thinning must necessarily be combined with fertilization. Thinning without fertilization and fertilization without thinning show the worst results. In general, findings of this article can be used to improve approach of thinning treatment in the Leningrad or other regions in the North of Russian Federation.

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“…There is no doubt that intensive thinning leads to a better use of nutrient resources [23][24][25][26].…”

Section: Figure 2 Age-related Changes In Total Yield At Low-thinned A...mentioning

confidence: 99%

Comparative Analysis of Thinning Techniques in Garchinsky Forestry

Grigoreva¹,

Рунова²,

Стородубцева³

et al. 2022

MMEP

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“…In the ILSVRC-2010 (ImageNet Large-Scale Visual Recognition Challenge-2010), Yuan, Chiang, Tang, and Haro [3] trained a DL model to perform classification of images, accomplishing best-in-class results. The impact of deep configuration depth on machine vision efficiency was discussed by Kunickaya et al, [4]. This framework has already sparked a lot of interest in applying this novel technique to clinical computer vision problems, thanks to these productive research findings.…”

Section: Introductionmentioning

confidence: 99%

A Critical Analysis of Biomedical Image Classification on Deep Learning

Pan

2021

JBSHA

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In computer-aided diagnostic technologies, deep convolutional neural image compression classifications are a crucial method. Conventional methods rely primarily on form, colouring, or feature descriptors, and also their configurations, the majority of which would be problem-specific that has been depicted to be supplementary in image data, resulting in a framework that cannot symbolize high problem entities and has poor prototype generalization capability. Emerging Deep Learning (DL) techniques have made it possible to build an end-to-end model, which could potentially general the last detection framework from the raw clinical image dataset. DL methods, on the other hand, suffer from the high computing constraints and costs in analytical modelling and streams owing to the increased mode of accuracy of clinical images and minimal sizes of data. To effectively mitigate these concerns, we provide a techniques and paradigm for DL that blends high-level characteristics generated from a deep network with some classical features in this research. The following stages are involved in constructing the suggested model: Firstly, we supervisedly train a DL model as a coding system, and as a consequence, it could convert raw pixels of medical images into feature extraction, which possibly reflect high-level ideologies for image categorization. Secondly, using image data background information, we derive a collection of conventional characteristics. Lastly, to combine the multiple feature groups produced during the first and second phases, we develop an appropriate method based on deep neural networks. Reference medical imaging datasets are used to assess the suggested method. We get total categorization reliability of 90.1 percent and 90.2 percent, which is greater than existing effective approaches.

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“…The purpose of ISO 17225-4:2021 is to establish an unambiguous and transparent classification standard for graded wood chips. Several attributes fall under the wood chip quality standards, including MC [2,3,[5][6][7], ash content [5,18,19,28,33,41], particle size distribution [18,26,[46][47][48][49][50][51][52][53], and the amount of some inorganic elements such as chlorine.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review on wood chip moisture content assessment and prediction

Rahman,

Marufuzzaman,

Street

et al. 2024

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Wood chips are the primary sources of raw materials for numerous industries, including pelleting mills, biorefineries, pulp-and-paper industries, and biomass-based power generation facilities. Unfortunately, when wood chips are utilized as a renewable and environmentally friendly resource, industries are constantly challenged by the consistency of the wood chip qualities (e.g., moisture/ash contents, size distributions) - a historically recognized problem on a global scale. Among other wood chip quality attributes, the moisture content is considered the most pressing one as it directly impacts the energy content, storage stability, and handling properties of the raw and finished products. Therefore, accurate wood chip moisture content prediction can help optimize the drying process and reduce energy consumption. In this review, a survey was conducted on various techniques and models employed for predicting wood chip moisture content. The advantages and limitations of these approaches, as well as their potential applications and future directions were also discussed. This review aims to provide a comprehensive overview of the current state-of-the-art in wood chip moisture content prediction and to highlight the challenges and opportunities for further research and development in this field.

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Using Machine Vision to Improve the Efficiency of Lumber Mills

Cited by 6 publications

References 9 publications

Comparative Analysis of Thinning Techniques in Garchinsky Forestry

Comparative Analysis of Thinning Techniques in Garchinsky Forestry

A Critical Analysis of Biomedical Image Classification on Deep Learning

A comprehensive review on wood chip moisture content assessment and prediction

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