Application of a hybrid neural network for the discrimination of seeds by artificial vision

Abstract

Intelligent hybrid systems are playing an increasing role in the development of artificial intelligence. In this study, we applied simulated annealing to adjust the weights of a multilayer neural network (MNN). Two versions of simulated annealing were tested: conventional simulated annealing (CSA) and fast simulated annealing (FSA). The applied hybrid system was used as a classifier in order to discriminate between 3 seed species (1 cultivated seed species which is perennial rye grass, and 2 adventitious seed species which are rumex and wild oat). From a set of colour digital images, 73 morphometrical and textural features were extracted to characterise each individual seed. Stepwise discriminant analysis made it possible to select the first 3 relevant features. The performances of classification were highly dependent on the scaling parameters of simulated annealing. For example, when the number of iterations of simulated annealing was 5, and the number of temperatures was 40, the combination between CSA and MNN correctly classified 98.18 and 97.77 percent of the training and the test sets, whereas FSA and MNN identified 99.18 and 99.68 percent of the same data sets. Globally, FSA outperformed CSA both in reliability and computational resources. A hybrid system combined with a colour image analysis showed promise for the design of an automatic seed identification device.