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This article in CS

  1. Vol. 51 No. 1, p. 21-31
     
    Received: Feb 4, 2010


    * Corresponding author(s): ckimbeng@agcenter.lsu.edu
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doi:10.2135/cropsci2010.02.0057

Artificial Neural Network Models as a Decision Support Tool for Selection in Sugarcane: A Case Study Using Seedling Populations

  1. Marvellous M. Zhouab,
  2. Collins A. Kimbeng *a,
  3. Thomas L. Tewc,
  4. Kenneth A. Gravoisd and
  5. Michael J. Pontifd
  1. a School of Plant, Environmental and Soil Sciences, Louisiana State Univ. Agricultural Center, 104 M.B. Sturgis Hall, Baton Rouge, LA 70803
    b current address: South African Sugarcane Research Institute, 170 Flanders Dr., Private Bag X02, Mount Edgecombe, KwaZulu-Natal, ZA 4300
    c USDA-ARS, Sugarcane Research Unit, 5883 USDA Rd., Houma, LA 70360
    d Sugar Research Station, Louisiana State Univ. Agricultural Center, 5755 LSU Ag Rd., St Gabriel, LA 70776

Abstract

Artificial neural network (ANN) models are mathematical models based on biological neural networks; they are a supervised learning method and use pattern learning from a training dataset that is a subsample of the whole dataset to produce predictions of response variables. We demonstrate the potential of an ANN model as a tool for selection in sugarcane. Cane yield components, namely stalk number, stalk height, and stalk diameter, were measured on individual seedlings and used as predictor variables to produce a selection decision (reject or select a seedling) based on an ANN model. Compared with the currently used visual method of selection, the difference in cane yield between the mean of the selected and rejected seedlings was greater for seedlings selected by the ANN model. The difference increased when similar selection intensity was applied in both selection methods. The ANN model selected fewer seedlings with cane yield lower than the population mean and rejected fewer seedlings with higher cane yield compared with the visual method. The ANN model compels the breeder to consider all traits simultaneously when deciding whether to select or reject a clone, which is likely to be more efficient than judging the merit by considering each trait independently. The ANN model can be a valuable tool to determine selection rates to be applied in selecting sugarcane families during seedling selection.

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