ArticleFood chemistry. Molecular sciences2021
Artificial neural networks and genetic dissimilarity among saladette type dwarf tomato plant populations.
Article in Food chemistry. Molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed, 16 citations in OpenAlex.
- Integrating combining ability and multivariate analysis for the selection of superior tomato hybrids.Frontiers in plant science · 2026Article
- Introgression of the Self-Pruning Gene into Dwarf Tomatoes to Obtain Salad-Type Determinate Growth Lines.Plants (Basel, Switzerland) · 2024Article
- Productive and Physico-Chemical Parameters of Tomato Fruits Submitted to Fertigation Doses with Water Treated with Very Low-Frequency Electromagnetic Resonance Fields.Plants (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Studies have shown that dwarf plants have the potential for use in obtaining hybrids. The aim of this study was to evaluate the agronomic potential and genetic dissimilarity of saladette type dwarf tomato plant populations through the use of artificial neural networks (ANNs). The following traits were analyzed: mean fruit weight, transverse and longitudinal fruit diameter, fruit shape, pulp thickness, locule number, internode length, soluble solids content, and β-carotene, lycopene, and leaf zingiberene contents. A dendrogram obtained by the unweighted pair-group method with arithmetic mean (UPGMA) and Kohonen self-organizing maps (SOM) agreed in the distinction of the BC
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