An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat

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.

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Author Alkhudaydi, Tahani
Last Updated October 30, 2019, 17:32 (UTC)
Created October 30, 2019, 17:32 (UTC)
Article Host Type publisher
Article Is Open Access true
Article License Type cc-by
Article Version Type publishedVersion
Citation Report https://scite.ai/reports/10.34133/2019/7368761
DFW Organisation EI
DFW Work Package 4
DOI 10.34133/2019/7368761
Date Last Updated 2019-09-28T14:23:32.764707
Evidence open (via page says license)
Journal Is Open Access true
Open Access Status gold
PDF URL http://downloads.spj.sciencemag.org/plantphenomics/2019/7368761.pdf
Publisher URL https://doi.org/10.34133/2019/7368761