Nasamu Musa
Research Subjects
Research Interests
Nasamu Musa has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Daniel Joseph Leybourne
Department of Evolution, Ecology and Behaviour, Institute of Infection, Veterinary and Ecological Sciences, Liverpool University, Liverpool, UK
1 shared publication - Katarzyna Dybal
Agriculture and Environment Department Harper Adams University Newport The United Kingdom
1 shared publication - Matthew A. Back
Agriculture and Environment Department, Harper Adams University, Newport TF10 8NB, UK
1 shared publication - Po Yang
University of Sheffield
1 shared publication - Zhipeng Yuan
University of Sheffield
1 shared publication
Latest Publications
- Open Access
Quantifying Nematodes through Images: Datasets, Models, and Baselines of Deep Learning
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.
Open AccessQuantifying Nematodes through Images: Datasets, Models, and Baselines of Deep Learning
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.