Health assessment of tree trunk by using acoustic-laser technique and sonic tomography

This study was conducted to count Apate terebrans opened tunnels in Terminalia mantaly at the 3 Parks of the University of Port Harcourt. All trees were assessed visually for the presence and absence of tunnels, frass, and exudates during the 6-months (January-July) field survey in 2018. The mean numbers of tunnels/tree were 16.12±2.13 from 58 trees representing 19.9% of 291 total trees. The spread of attack was fairly scattered and more closely associated with diameter at breast height (DBH) of tree (χ2= 12.763, df = 4, α0.012*). While a majority (67.0%) of the trees experienced breaking reflecting both the immediate and residual impact of opened and closed tunnels, respectively, 10.7% experienced tearing of branches, and 5.5% experienced both breaking and tearing. Strong wind was found as a contributory factor to breaking and tearing. The findings suggest T. mantaly as a poor adaptable environmental species in Nigeria ecosystems and its current utilisation as ornamental tree may be revisited. Contribution/Originality:This study documents the resurgence of wood borer, Apate terebrans and its impacts on the Terminalia mantaly used as one of the current popular urban trees in Nigeria. INTRODUCTIONA world-wide-spread problem in wood utilisation is related to the protection of wood products that are often attacked by insects. The same problem is encountered in the environmental utilisation of trees (living woods). Until now a satisfactory solution to control the trees' degradation by wood-boring beetles had not been found, and the applied conservation methods offered only temporary reduced controls [1, 2] without guaranteeing a real and durable effectiveness. Beetles are one of the major devastating pests of plants worldwide with many types economically, environmentally and socially attacking healthy trees [3,4]. Among these, wood-boring beetles are the most common disturbance of trees worldwide [5,6] with a high prevalence from Bostrichidae [7, 8] and higher incidence in introduced species than native ones [9]. The black beetle, Apate terebrans (Pallas, 1772) (Coleoptera: Bostrichidae), is a widely distributed pest in Africa and Neotropical climes [10,11]. Also, the occurrence of this beetle in Austria, and Europe has been reported [1]. Its primary hosts include many agricultural and forest trees [12]. In Nigeria, Murray [13] in 1867 listed A.

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“…Cavity defects in wood are a determinant of mechanical strength loss causing tree failure and human death in urban environments [36][37][38].…”

Section: Impacts Of Attackmentioning

confidence: 99%

A Survey of Wood Borer Apate terebrans Tunnels on Terminalia mantaly in Nigeria with Special Reference to the Niger Delta Region

Adedeji¹,

Aiyeloja²,

Zakka³

et al. 2021

Journal of Forests

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“…Today, a harvester operator visually monitors the advance of the decay in butt-rotten stems, which takes a considerable amount of cutting work (Figure 3). In the future, an application based on machine vision (cf., [69,70]), laser scanning (e.g., [71][72][73]), or tomography (e.g., [74,75]), or a combination, might assist operators to cross cut decayed stems with precision and improve their cutting productivity and the value recovery of timber from butt-rotten Norway spruce stands (cf., [35,76]).…”

Section: Evaluation Of the Study Findingsmentioning

confidence: 99%

The Profitability of Cross-Cutting Practices in Butt-Rotten Picea abies Final-Felling Stands

Kärhä¹,

Räsänen²,

Palander³

2020

Prime Archives in Environmental Research

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Research Highlights: This study offers new information on the cross cutting of decayed stems with the sounding of short (0.5 m) offcuts and the bucking of longer (3.0 m) butt-rotten poles. Background and Objectives: The root and butt-rot fungus Heterobasidion annosum sensu lato (Fr.) Bref. causes wood quality damage to trees in softwood forests. When timber is harvested in butt-rotten forests, it is essential that the decayed part of the tree is recognized and cut away from a stem, while the healthy and good quality log section of a stem is cross cut with precision sawlogs. The objective of the study was to investigate the impact of two off-cutting methods on stem processing time, cutting productivity, sawlog volume, and commercial value at the roadside landing when harvesting timber from the butt-rotten Norway spruce (Picea abies (L.) Karst.) final-felling forests. Materials and Methods:The length of the short offcuts used was 0.5 m. The results of the cross-cutting practices were compared to the decayed pulpwood poles of 3 m from the butt of the rotten stems. Time and motion studies were carried out in stands before the profitability calculations. The study data consisted of 1980 Norway spruce sawlog stems. Results: Sounding of the short offcuts added significantly to the stem processing time of butt-rotten stems, but the sawlog volume and the timber value recovery of the stems were higher than those of the decayed pulpwood poles of 3 m. Conclusions: The study concluded that sounding of butt-rotten Norway spruce stems with one to three offcuts is economically profitable if the diameter of the decayed column at the stem stump's height is small (≤5 cm). In contrast, when the width of the decay is larger (>5 cm), it is more profitable to first cross cut the decayed pulpwood pole of 3 m and then to observe the height of the decayed part of the stem.

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“…In recent years, various methods based on machine vision and computer science have been developed to detect the quality of wood. Non-destructive wood testing methods that are now commonly used include near-infrared spectroscopy testing [ 4 , 5 , 6 ], ultrasonic testing [ 7 , 8 , 9 ], X-ray testing [ 10 , 11 ], laser testing [ 12 , 13 ], and acoustic emission technology [ 14 , 15 , 16 ]. Good results have been obtained by combining the above methods of extracting the surface or internal features of wood with classic machine learning methods, such as back propagation neural network (BP), support vector machine (SVM), and K-means clustering algorithm to predict and classify wood features.…”

Section: Introductionmentioning

confidence: 99%

Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm

Ding

Zhuang

Liu

et al. 2020

Sensors

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Wood is widely used in construction, the home, and art applications all over the world because of its good mechanical properties and aesthetic value. However, because the growth and preservation of wood are greatly affected by the environment, it often contains different types of defects that affect its performance and ornamental value. To solve the issues of high labor costs and low efficiency in the detection of wood defects, we used machine vision and deep learning methods in this work. A color charge-coupled device camera was used to collect the surface images of two types of wood from Akagi and Pinus sylvestris trees. A total of 500 images with a size of 200 × 200 pixels containing wood knots, dead knots, and checking defects were obtained. The transfer learning method was used to apply the single-shot multibox detector (SSD), a target detection algorithm and the DenseNet network was introduced to improve the algorithm. The mean average precision for detecting the three types of defects, live knots, dead knots and checking was 96.1%.

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Health assessment of tree trunk by using acoustic-laser technique and sonic tomography

Cited by 25 publications

References 44 publications

A Survey of Wood Borer Apate terebrans Tunnels on Terminalia mantaly in Nigeria with Special Reference to the Niger Delta Region

A Survey of Wood Borer Apate terebrans Tunnels on Terminalia mantaly in Nigeria with Special Reference to the Niger Delta Region

The Profitability of Cross-Cutting Practices in Butt-Rotten Picea abies Final-Felling Stands

Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm

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