Segmentation of Wheat Grains in Thermal Images Based on Pulse Coupled Neural Networks

Canada is one of the major exporters of wheat in the world. The quality of these exports is well known and factors such as lack of insect infestation are very important. The use of thermal images for subsequent analysis of temperatures profiles for grain classification and insect detection is a method under investigation. This paper presents an approach for automatic image segmentation of the wheat kernels based on the combined use of wavelet analysis and pulse coupled neural networks. It is shown that using wavelets as a preprocessing technique yields a consistent accurate segmentation in terms of the iteration number in which the network yields reliable edges of the wheat kernels. Subsequent analysis of these segmentations can determine internal qualities such as infestations.