Conditioning of Scots pine (<i>Pinus sylvestris</i> L.) sowing material

Szajsner, Hanna; Bąbelewski, P.; Koszelnik-Leszek, Anna; Kuryś, Magdalena

doi:10.1080/02827581.2023.2177335

Cited by 1 publication

(1 citation statement)

References 27 publications

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“…While they have been used for almost two decades in stem taper predictions, in recent years there was a renewed interest for neural networks to accomplish this task. Neural networks were observed to perform worse than parametric models for Pinus sylvestris [40] and Tectona grandis [41], however they performed better for Acacia decurrens [36], Fagus orientalis [42], Abies nordmanniana [42], Pinus sylvestris [43], Pinus taeda [44], three Nothofagus species [45], seven species in Poland [46] and multiple Brazilian species [37]. While having a greater capacity to model complex relationships than traditional parametric models, the neural networks that were developed for stem taper prediction are not ideal for stem bucking.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic stem bucking using mixture density neural networks

Simon

2024

Preprint

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Poor bucking decisions made by forest harvesters can have a negative effect on the products that are generated from the logs. Making the right bucking decisions is not an easy task because harvesters must rely on predictions of the stem profile for the part of the stems that is not yet measured. The goal of this project is to improve the bucking decisions made by forest harvesters with a stochastic bucking method. We developed a Long Short-Term Memory (LSTM) neural network that predicted the parameters of a Gaussian distribution conditioned on the known part of the stem, enabling the creation of multiple samples of stem profile predictions for the unknown part of the stem. The bucking decisions could then be optimized using a novel stochastic bucking algorithm which used all the stem profiles generated to choose the logs to generate from the stem. The stochastic bucking algorithm was compared to two benchmark models: A polynomial model that could not condition its predictions on more than one diameter measurement, and a deterministic LSTM neural network. All models were evaluated on stem profiles of four coniferous species prevalent in eastern Canada. In general, the best bucking decisions were taken by the stochastic LSTM models, demonstrating the usefulness of the method. The second-best results were mostly obtained by the deterministic LSTM model and the worst results by the polynomial model, corroborating the usefulness of conditioning the stem curve predictions on multiple measurements.

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Section: Literature Reviewmentioning

confidence: 99%

Stochastic stem bucking using mixture density neural networks

Simon

2024

Preprint

View full text Add to dashboard Cite

show abstract

Conditioning of Scots pine (Pinus sylvestris L.) sowing material

Cited by 1 publication

References 27 publications

Stochastic stem bucking using mixture density neural networks

Stochastic stem bucking using mixture density neural networks

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