Evaluating Variation in Seed Quality Attributes in Pinus Patula Clonal Orchards using Cone Cluster Analysis

Owino, Jesse Omondi; Angaine, Peter Murithi; Onyango, Alice Adongo; Ojunga, Samson Okoth; Otuoma, John

doi:10.18488/journal.101.2020.71.1.8

Cited by 2 publications

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“…The percentage of the cone that opens after drying for seed extraction (p) was calculated using Eq. (1); this p is used to compare the opening length of the cone in relation to its shape [19,21,24].…”

Section: Pinus Patulamentioning

confidence: 99%

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Seed production and handling of two important conifers grown in Kenya

Murithi Angaine,

Adongo Onyango,

Owino

2023

Seed Biology - New Advances

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Pinus patula and Cupressus lusitanica are key commercial forestry plantation species introduced in Kenya. There are many uses for these species in industry creating a huge demand for their products. The demand has caused increased plantation establishment with seed as the major source of propagule. The many developments in the forestry sector have led to the need for low energy rapid extraction technique that improve seed quantity and quality from the available sources. There have been developments in improving extraction and quality which offer an opportunity for better seed collection and handling techniques for these conifers. This chapter will focus on improvement of seed production and handling of the two conifers that aids in the design of low energy-intensive methods that reduce the duration for extraction, optimize seed yield and enhance seed quality.

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Section: Pinus Patulamentioning

confidence: 99%

“…Mathematical modeling was used to analyze the results, which showed that the length of the cone had the greatest influence on seed yield [24].…”

Section: Pinus Patulamentioning

confidence: 99%

Seed production and handling of two important conifers grown in Kenya

Murithi Angaine,

Adongo Onyango,

Owino

2023

Seed Biology - New Advances

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Using machine learning algorithms to cluster and classify stone pine (Pinus pinea L.) populations based on seed and seedling characteristics

Caliskan,

Kartal,

Balekoglu

et al. 2024

Eur J Forest Res

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The phenotype of a woody plant represents its unique morphological properties. Population discrimination and individual classification are crucial for breeding populations and conserving genetic diversity. Machine Learning (ML) algorithms are gaining traction as powerful tools for predicting phenotypes. The present study is focused on classifying and clustering the seeds and seedlings in terms of morphological characteristics using ML algorithms. In addition, the k-means algorithm is used to determine the ideal number of clusters. The results obtained from the k-means algorithm were then compared with reality. The best classification performance achieved by the Random Forest algorithm was an accuracy of 0.648 and an F1-Score of 0.658 for the seed traits. Also, the best classification performance for stone pine seedlings was observed for the k-Nearest Neighbors algorithm (k = 18), for which the accuracy and F1-Score were 0.571 and 0.582, respectively. The best clustering performance was achieved with k = 2 for the seed (average Silhouette index = 0.48) and seedling (average Silhouette Index = 0.51) traits. According to the principal component analysis, two dimensions accounted for 97% and 63% of the traits of seeds and seedlings, respectively. The most important features between the seed and seedling traits were cone weight and bud set, respectively. This study will provide a foundation and motivation for future efforts in forest management practices, particularly regarding reforestation, yield optimization, and breeding programs.

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Evaluating Variation in Seed Quality Attributes in Pinus Patula Clonal Orchards using Cone Cluster Analysis

Cited by 2 publications

References 0 publications

Seed production and handling of two important conifers grown in Kenya

Seed production and handling of two important conifers grown in Kenya

Using machine learning algorithms to cluster and classify stone pine (Pinus pinea L.) populations based on seed and seedling characteristics

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