Transfer Learning using Hybrid Convolution and Attention Model for Nematode Identification in Soil Ecology

Abstract

Nematodes constitute a crucial component of soil ecosystems, contributing significantly to soil ecology. The ability to differentiate between non-parasitic and plant-parasitic species is a critical aspect of efficient crop management. However, manual identification methods are labor-intensive, time-consuming, and susceptible to errors. Recent developments in the realm of machine and deep learning have paved the way for their application in the field of nematode identification. This study implements a hybrid convolution and attention network, termed CoAtNet-0, which integrates a Convolutional Neural Network (CNN) and a transformer for the identification of nematode genera. The performance of this model is anticipated to be robust across various dataset sizes. The current investigation employed a combined dataset, comprising both a self-collected nematode dataset and a public dataset, to evaluate the performance of CoAtNet-0 under single and double data augmentation conditions. Furthermore, it explored the efficacy of the Adam, Stochastic Gradient Descent (SGD), and RMS optimizers to identify the most effective optimizer for the CoAtNet-0 model. Adam was selected due to its typically satisfactory performance, while SGD was chosen as it often delivers superior results in deep learning applications. RMSprop was utilized for performance comparison among adaptive optimizers devoid of momentum. Upon evaluation, it was determined that the highest performance was achieved by the CoAtNet-0 model using the SGD optimizer on the non-augmented dataset, delivering an accuracy of 97.22%. Thus, the selection of suitable data augmentation methods and an appropriate optimizer is instrumental in optimizing the performance of the CoAtNet-0 model for nematode identification.