Distinguishing Cyst Nematode Species Using Image Textures and Artificial Neural Networks

Abstract

Cyst nematodes are plant parasitic nematodes infecting crops, causing extensive crop damage and annual losses, and affecting food production. The precise species identification is significant to initiate their control. The repeatable, less expensive, and less laborious distinguishing cyst nematode species using image processing and artificial intelligence can be advantageous. The objective of this study was to distinguish cyst nematodes belonging to the species Globodera pallida, Globodera rostochiensis, and Heterodera schachtii based on image parameters using artificial neural networks (ANN). The application of parameters selected from a set of 2172 textures of images in color channels L, a, b, X, Y, Z, R, G, B, V, U, and S to build classification models using a narrow neural network, medium neural network, wide neural network, trilayered neural network, WiSARD, multilayer perceptron, and RBF network is a great novelty of the present study. Algorithms allowed for distinguishing cyst nematode species with an average accuracy reaching 89.67% for a model developed using WiSARD. The highest correctness was obtained for H. schachtii and this species was distinguished from each other with the highest accuracy of 95–98% depending on the classifier. Whereas the highest number of misclassified cases occurred between G. pallida, G. rostochiensis belonging to the same genus Globodera. The developed procedure involving image parameters and artificial neural networks can be useful for non-destructive and objective distinguishing cyst nematode species.