scholarly journals Classification of Oil Palm Female Inflorescences Anthesis Stages Using Machine Learning Approaches

2020 ◽  
Author(s):  
Mamehgol Yousefi ◽  
Azmin Shakrine ◽  
Samsuzana bt. Abd Aziz ◽  
Syaril Azrad ◽  
Mohamed Mazmira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Oil Palm ◽  
Learning Approaches

scholarly journals Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dennie te Molder ◽  
Wasin Poncheewin ◽  
Peter J. Schaap ◽  
Jasper J. Koehorst
Keyword(s):  
Machine Learning ◽  
Plant Pathogens ◽  
De Novo ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Enabling Factors ◽  
Essential Step ◽  
The World ◽  
Genome Content

Abstract Background The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step. Results Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest. Conclusion The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.

scholarly journals Predicting Heritability of Oil Palm Breeding Using Phenotypic Traits and Machine Learning

2021 ◽  
Vol 13 (22) ◽  
pp. 12613
Author(s):  
Najihah Ahmad Latif ◽  
Fatini Nadhirah Mohd Nain ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Rosni Abdullah ◽  
Muhammad Farid Abdul Rahim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Conceptual Framework ◽  
Oil Palm ◽  
Crop Yields ◽  
Phenotypic Traits ◽  
Learning Approaches ◽  
Data Types ◽  
Phenotypic Data ◽  
Fresh Fruit Bunch ◽  
The Sustainable Development

Oil palm is one of the main crops grown to help achieve sustainability in Malaysia. The selection of the best breeds will produce quality crops and increase crop yields. This study aimed to examine machine learning (ML) in oil palm breeding (OPB) using factors other than genetic data. A new conceptual framework to adopt the ML in OPB will be presented at the end of this paper. At first, data types, phenotype traits, current ML models, and evaluation technique will be identified through a literature survey. This study found that the phenotype and genotype data are widely used in oil palm breeding programs. The average bunch weight, bunch number, and fresh fruit bunch are the most important characteristics that can influence the genetic improvement of progenies. Although machine learning approaches have been applied to increase the productivity of the crop, most studies focus on molecular markers or genotypes for plant breeding, rather than on phenotype. Theoretically, the use of phenotypic data related to offspring should predict high breeding values by using ML. Therefore, a new ML conceptual framework to study the phenotype and progeny data of oil palm breeds will be discussed in relation to achieving the Sustainable Development Goals (SDGs).

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