Neural network model for the evaluation of lettuce plant growth

A back propagation neural network was trained to evaluate lettuces in terms of plant growth characteristics, with a network consisting of 7, 8 and 5 p rocessing units in the input, hidden and output layer, respectively. To gen erate the training data, clinorotation rates in the range between 0 and 25 rpm, centrifugation rates in the range between 0 and 5.5 rpm were selected for experiments to measure the daily plant width and height after transplan t. Fifty-eight sets of training data were used. The training was terminated after 22124 times of iterative calculations at the root mean square error value equal to 4.02 x 10(-4). Ten sets of validation data were used to calc ulate the prediction error. The average prediction error was in the range b etween 25 and 9.7%. The ability of the neural network models to predict the required information is very accurate. As a result, there is a potential f or the present technique to be applied to plant growth evaluating system un der the simulated gravity conditions. (C) 1999 Silsoe Research Institute.