Neural Network Model for the Evaluation of Lettuce Plant Growth

✍ Scribed by M.A. Zaidi; H. Murase; N. Honami

• Publisher: Elsevier Science
• Year: 1999
• Tongue: English
• Weight: 198 KB
• Volume: 74
• Category: Article
• ISSN: 0021-8634
• DOI: 10.1006/jaer.1999.0452

✦ Synopsis

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 processing units in the input, hidden and output layer, respectively. To generate 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 transplant. Fifty-eight sets of training data were used. The training was terminated after 22 124 times of iterative calculations at the root mean square error value equal to 4)02;10U. Ten sets of validation data were used to calculate the prediction error. The average prediction error was in the range between 2)5 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 for the present technique to be applied to plant growth evaluating system under the simulated gravity conditions.