  Every year, plant parasitic nematodes, one of the major groups of plant
pathogens, cause a significant loss of crops worldwide. To mitigate crop yield
losses caused by nematodes, an efficient nematode monitoring method is
essential for plant and crop disease management. In other respects, efficient
nematode detection contributes to medical research and drug discovery, as
nematodes are model organisms. With the rapid development of computer
technology, computer vision techniques provide a feasible solution for
quantifying nematodes or nematode infections. In this paper, we survey and
categorise the studies and available datasets on nematode detection through
deep-learning models. To stimulate progress in related research, this survey
presents the potential state-of-the-art object detection models, training
techniques, optimisation techniques, and evaluation metrics for deep learning
beginners. Moreover, seven state-of-the-art object detection models are
validated on three public datasets and the AgriNema dataset for plant parasitic
nematodes to construct a baseline for nematode detection.
