Non-Linear Discrimination of Days After Planting of Lettuce by Using Image Information

Abstract

This research is a fundamental analysis of a growth monitoring method for plant production control. Two varieties of lettuce, Telme lettuce and Tango lettuce, were made into the object in this research. The number of days after planting of the lettuce was determined by image analysis and the neural network. For Telme lettuce, the 10th, 20th and 30th days after planting were set as the target dates to note. For Tango lettuce, the 7th, 14th and 21st days were fixed as objectives. In the previous analysis, the number of days was recognized by linear discriminant analysis. The error rate of the recognition by the linear method was 2.5% for Telme lettuce and 20% for Tango lettuce. The Tango lettuce showed a large individual difference in the image features. Thus, the features were distributed widely in the feature space and there were many intersections among the three groups. It was difficult to separate the space into three parts by the discriminant planes. Therefore, the non-linear method was adopted. The accuracy of the discrimination was improved by the neural network. The error rate decreased down to 1.00% for Telme lettuce and 14.7% for Tango lettuce. These results show the high feasibility of incorporating the discriminant algorithm into the monitoring instrumentation for the growth control system.