Kohonen self-organizing map for ideotype identification chickpea agronomic
Keywords:
Cicer arietinum L., Sowing dates, Productivity, NeuronsAbstract
The objective of this work was to identify the agronomic ideotype of chickpeas (Cicer arietinum L.) with high productive performance. Uniformity trials were carried out with the chickpea crop, cultivar BRS Cristalino, in Santa Maria, state of Rio Grande do Sul. Each trials was composed of four rows of plants 5 m long and spaced at 0. 5 m between rows. In each trial, 20 plants were randomly marked in the two central rows, totaling 160 plants. The following characters were evaluated in these plants: plant height (PH), number of leaves (NL), number of nodes (NN), number of primary branches (NPB), number of secondary branches (NSB), number of pods without grains (NPWG), number of pods with one grain (NP1G), number of pods with two grains (NP2G), number of pods (NP), number of grains (NG) and grain productivity (PROD). Kohonen's self organizing map was used to identify the agronomic ideotype of chickpea. A 3 × 3 neural structure was used, obtaining nine neurons. The hexagonal topology was used for the configuration, and the data organization was performed with 300 iterations. The chickpea agronomic ideotype that maximizes grain productivity is characterized by plants with higher values of number of grains, number of pods, number of pods with one grain and number of primary branches and lower values of number of pods without grains, number of pods with two grains, number of secondary branches, number of nodes, number of leaves and plant height.
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