Transfer Learning Techniques to Classify Nematodes Species

Abstract

Phytoparasitic nematodes are severely damaging crops all over the world, which leads to an enormous financial loss. Some researchers estimate that less than 0.01 % of these species have not yet been discovered. Since most nematodes have similar physical traits, it can be difficult to classify them using traditional techniques. In the past, the only way to identify nematodes was through their morphological traits, including body length, their reproductive organs' arrangement, and other physical characteristics. The aforementioned method is exceedingly labor and skill-intensive, and its classification is solely dependent on human ability and costly machinery. In recent years, DL-based techniques have greatly enhanced and boosted accuracy. Using DL algorithms InceptionV3 and VGG16, these species were effectively categorized in this study. Five different species of nematodes, Acrobeles, Acrobeloides, Aphelenchoides, Amplimerlinius, and Discolimus, were used. The given dataset, which consists of 1500 digital photos of nematodes, is further expanded to 5000 images using data augmentation techniques like flipping, shearing, zooming, and other procedures. Two pre-trained CNN models, InceptionV3 and VGG16, have been improved to classify these species. The InceptionV3 and VGG16 models have respective accuracy rates of 98.02 % and 95.87 %.