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CNN-Based Cassava Storage Root Counting using Real and Synthetic Images (2019)
Journal Article
Atanbori, J., Montoya, M., Selvaraj, M. G., French, A. P., & Pridmore, T. P. (2019). CNN-Based Cassava Storage Root Counting using Real and Synthetic Images. Frontiers in Plant Science, 10, https://doi.org/10.3389/fpls.2019.01516

Cassava roots are complex structures comprising several distinct types of root. The number and size of the storage roots are two potential phenotypic traits reflecting crop yield and quality. Counting and measuring the size of cassava storage roots a... Read More about CNN-Based Cassava Storage Root Counting using Real and Synthetic Images.

A low-cost aeroponic phenotyping system for storage root development: unravelling the below-ground secrets of cassava (Manihot esculenta) (2019)
Journal Article
Montoya-P, M. E., Selvaraj, M. G., French, A. P., Atanbori, J., & Pridmore, T. (2019). A low-cost aeroponic phenotyping system for storage root development: unravelling the below-ground secrets of cassava (Manihot esculenta). Plant Methods, 15(1), https://doi.org/10.1186/s13007-019-0517-6

Background Root and tuber crops are becoming more important for their high source of carbohydrates, next to cereals. Despite their commercial impact, there are significant knowledge gaps about the environmental and inherent regulation of storage roo... Read More about A low-cost aeroponic phenotyping system for storage root development: unravelling the below-ground secrets of cassava (Manihot esculenta).

Towards Low-Cost Image-based Plant Phenotyping using Reduced-Parameter CNN (2018)
Conference Proceeding
Atanbori, J., Chen, F., French, A. P., & Pridmore, T. (2018). Towards Low-Cost Image-based Plant Phenotyping using Reduced-Parameter CNN. In Proceedings of BMVC 2018

Segmentation is the core of most plant phenotyping applications. Current state-of-the-art plant phenotyping applications rely on deep Convolutional Neural Networks (CNNs). However, these networks have many layers and parameters, increasing training a... Read More about Towards Low-Cost Image-based Plant Phenotyping using Reduced-Parameter CNN.

Classification of bird species from video using appearance and motion features (2018)
Journal Article
Atanbori, J., Duan, W., Shaw, E., Appiah, K., & Dickinson, P. (2018). Classification of bird species from video using appearance and motion features. Ecological informatics, 48, 12-23. https://doi.org/10.1016/j.ecoinf.2018.07.005

The monitoring of bird populations can provide important information on the state of sensitive ecosystems; however, the manual collection of reliable population data is labour-intensive, time-consuming, and potentially error prone. Automated monitori... Read More about Classification of bird species from video using appearance and motion features.

A computer vision approach to classification of birds in flight from video sequences (2015)
Conference Proceeding
Atanbori, J., Duan, W., Murray, J., Appiah, K., & Dickinson, P. (2015). A computer vision approach to classification of birds in flight from video sequences. In Proceedings of the Machine Vision of Animals and their Behaviour (MVAB), 3.1-3.9. doi:10.5244/c.29.mvab.3

Bird populations are an important bio-indicator, ; so collecting reliable data is useful for ecologists helping conserve and manage fragile ecosystems. However, existing manual monitoring methods are labour-intensive, time-consuming, and error-prone.... Read More about A computer vision approach to classification of birds in flight from video sequences.

Automatic classification of flying bird species using computer vision techniques (2015)
Journal Article
Atanbori, J., Duan, W., Murray, J., Appiah, K., & Dickinson, P. (2016). Automatic classification of flying bird species using computer vision techniques. Pattern recognition letters, 81, (53-62). doi:10.1016/j.patrec.2015.08.015. ISSN 0167-8655

Bird populations are identified as important biodiversity indicators, so collecting reliable population data is important to ecologists and scientists. However, existing manual monitoring methods are labour-intensive, time-consuming, and potentially... Read More about Automatic classification of flying bird species using computer vision techniques.


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