ABSTRACT
From five paddy rice cultivars grown in Taiwan and harvested in the summers of 1997, 1998, and 1999, five calibrated models were established by backpropagation neural network program through different morphological and color features selection for classifying paddy rice harvested in the summer of 2000. With 60 features, the average classification rates of Model 1 and Model 5 were 92 and 99.8%, respectively. With the most effective 50 features, by loading in the first principal component, the average classification rate of Model 2 was 90.0%. With 35 features selected from the correlation coefficient matrix, the average classification rate of Model 3 was 91.0%. With the most effective 20 features of area, area/perimeter, 48th width, shape factor, maximum length/maximum width, average intensity of blue, maximum length, average intensity of green, 47th width, 50th width, average intensity of red, 1st width, 19th width, 5th width, 6th width, 29th width, perimeter, 46th width, 42nd width, and 4th width based on the contribution of the training model, the average classification rate of Model 4 was 91.8% and would be recommended for classifying five paddy rice cultivars of set trading prices because it required fewer features and held a stable classification rate.
