Abstract
Both blemish and physical attributes were acquired on commercially graded Florida grapefruit, orange, andtangerine varieties. Using equal numbers of acceptable and rejected fruit, various neural network classification strategieswere applied to blemishrelated features and blemish plus physical features. The blemish plus physical feature neural netmodels were the most successful, yielding overall correct classification levels of 98.5% for grapefruit and orange and 98.3%for tangerine. No significant difference was found between the neural net models of standard backpropagation, jump step,or variable transfer functions for the hidden layer.
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