Boniecki, P., & Piekarska-Boniecka, H. 2004 The SOFM type neural networks in the process of identification of selected orchard pests.. Journal of Research and Applications in Agricultural Engineering 49(4): 5-9.

Notes: One of the advantages of the Kohonen type neural network, called also as SOFM (Self Organizing Feature Maps), is the ability of the discussed neural network to determine the degree of similarity occurring between classes. The SOFM network can be also used to detect regularities occurring in the obtained empirical data. If at the network input, a new unknown case appears which the network is unable to recognize, it means that it is different from all the classes known previously. The SOFM network taught in this way can serve as a detector signaling the appearance of a widely understood novelty. Such a network can also look for similarities between the known data and the noisy data. In this way, it is able to identify fragments of images presenting photographs of e.g. orchard pests. The purpose of this research was to use the SOFM neural networks in the process of identification of 5 selected orchard pests, namely Dasyneura mali, D. piri, Parthenolecanium corni, Zeuzera pyrina and Cossus cossus. The desirable features enable the Kohonen neural network to identify the pests correctly based on the presentation of images not originating from the teaching set, i.e. noisy photographs taken under different light exposure conditions and using different qualities of the equipment.