An exploration of deep-learning based phenotypic analysis to detect spike regions in field conditions for UK bread wheat

Alkhudaydi, Tahani, Reynolds, Daniel, Griffiths, Simon, Zhou, Ji and De La Iglesia, Beatriz ORCID: https://orcid.org/0000-0003-2675-5826 (2019) An exploration of deep-learning based phenotypic analysis to detect spike regions in field conditions for UK bread wheat. Plant Phenomics, 2019 (July). pp. 1-17. ISSN 2643-6515

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Abstract

Wheat is one of the major crops in the world, with a global demand expected to reach 850 million tons by 2050 that is clearly outpacing current supply. The continual pressure to sustain wheat yield due to the world’s growing population under fluctuating climate conditions requires breeders to increase yield and yield stability across environments. We are working to integrate deep learning into field-based phenotypic analysis to assist breeders in this endeavour. We have utilised wheat images collected by distributed CropQuant phenotyping workstations deployed for multiyear field experiments of UK bread wheat varieties. Based on these image series, we have developed a deep-learning based analysis pipeline to segment spike regions from complicated backgrounds. As a first step towards robust measurement of key yield traits in the field, we present a promising approach that employ Fully Convolutional Network (FCN) to perform semantic segmentation of images to segment wheat spike regions. We also demonstrate the benefits of transfer learning through the use of parameters obtained from other image datasets. We found that the FCN architecture had achieved a Mean classification Accuracy (MA) >82% on validation data and >76% on test data and Mean Intersection over Union value (MIoU) >73% on validation data and and >64% on test datasets. Through this phenomics research, we trust our attempt is likely to form a sound foundation for extracting key yield-related traits such as spikes per unit area and spikelet number per spike, which can be used to assist yield-focused wheat breeding objectives in near future.

Item Type:	Article
Uncontrolled Keywords:	wheat,crop phenomics,machine learning
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre (former - to 2023) Faculty of Science > Research Groups > Data Science and Statistics Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Related URLs:	http://dx.doi.org/10.34133/2019/7368761
Depositing User:	LivePure Connector
Date Deposited:	02 Aug 2019 08:30
Last Modified:	09 Mar 2024 01:16
URI:	https://ueaeprints.uea.ac.uk/id/eprint/71866
DOI:	10.34133/2019/7368761

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