Evidence map›Paper›PMID 41053131›Full record

ArticleScientific reports2025

Evaluating the effectiveness of the forest pests and diseases control methods on the industrial wood production using deep learning.

Volkan Sevinç

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Volkan SevinçDepartment of Statistics, Faculty of Science, Muğla Sıtkı Koçman University, Kötekli Kampüsü, 48000, Muğla, Turkey. vsevinc@mu.edu.tr.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Industrial wood production plays a vital role in the economies of many countries by supplying raw materials for a wide range of sectors, including construction, paper, and pulp industries. However, the industry is increasingly challenged by the detrimental effects of forest pests and diseases, which compromise the industrial wood quality and amount, ultimately resulting in substantial economic losses. To ensure sustainable wood production, the development and implementation of effective pest and disease management strategies are of crucial importance. Commonly employed control strategies encompass a range of methods, including the mechanical, the chemical, the biotechnical, and the biological methods. In this study, a deep learning model was developed to evaluate the effectiveness of these control methods on the industrial wood production output. The results of the model indicate that all four methods contribute positively to the industrial wood production, albeit to varying degrees. Among them, the chemical control methods were identified as the most effective in enhancing production levels, followed by the mechanical methods. The biological methods ranked third in terms of effectiveness, while the biotechnical methods were found to be the least effective in comparison to the other approaches. Although the chemical control methods are associated with well-documented environmental risks, their discontinuation in the short term remains impractical due to their high effectiveness and rapid action against pests and diseases. As an interim strategy, efforts should focus on minimizing their ecological impact through targeted research and the development of improved formulations and application techniques during the production phase. In the long term, however, it is imperative to prioritize investments and scientific studies aimed at enhancing the effectiveness and applicability of environmentally friendly alternatives. Such a transition is essential for achieving sustainable pest and disease management in the industrial wood production.

Indexed as

Deep LearningForestryForestsPest ControlPlant DiseasesWoodAnimalsArtificial neural networksBiological controlBiotechnical controlChemical controlDeep learningForest diseasesForest pestsIndustrial woodMachine learningMechanical control

Identifiers

PMID41053131
PMCPMC12501069

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.