Quantifying Nematodes through Images: Datasets, Models, and Baselines of Deep Learning

TL;DR

This paper surveys deep learning methods and datasets for nematode detection, validating seven models on multiple datasets to establish baselines, aiming to advance research in crop disease management and biological studies.

Contribution

It provides a comprehensive survey of datasets and models for nematode detection, and establishes baseline results with seven state-of-the-art object detection models.

Findings

Seven models validated on three datasets and AgriNema.

Baseline performance metrics established for nematode detection.

Survey and categorization of existing datasets and techniques.

Abstract

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.