An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat.
An Exploration of Deep-Learning Based Phenotypic Analysis to Detect Spike Regions in Field Conditions for UK Bread Wheat.
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DOI：
10.34133/2019/7368761
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
de la Iglesia B
中科院分区：
文献类型：
--
作者：
Alkhudaydi T;Reynolds D;Griffiths S;Zhou J;de la Iglesia B
小麦是世界上的主要农作物之一，预计到2050年，全球需求将达到8.5亿吨，显然超过了目前的供应。在多变的气候条件下，由于世界人口的增长，维持小麦产量的持续压力要求育种者提高产量和不同环境下的产量稳定性。我们正在努力将深度学习整合到基于田间的表型分析中，以帮助育种者进行这一努力。我们利用了分布式CropQuant表型工作站收集的小麦图像，这些工作站用于英国面包小麦品种的多年田间试验。基于这些图像序列，我们开发了一种基于深度学习的分析流水线，从复杂的背景中分割出棘波区域。作为在田间对关键产量性状进行稳健测量的第一步，我们提出了一种有前景的方法，该方法使用完全卷积网络(FCN)来执行图像的语义分割来分割小麦穗区域。我们还通过使用从其他图像数据集中获得的参数来演示转移学习的好处。我们发现，FCN架构在验证数据上达到了82%的平均分类准确率，在测试数据上达到了76%，在验证数据上达到了73%，在测试数据集上达到了AND>；。通过这项表型组学研究，我们相信我们的尝试可能会为提取关键的产量相关性状(如单位面积穗数和每穗小穗数)奠定坚实的基础，这些性状可以在不久的将来用于辅助以产量为重点的小麦育种目标。
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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DOI：
10.3390/s150304823
发表时间：
2015-02-26
期刊：
Sensors (Basel, Switzerland)
影响因子：
--
作者：
Kicherer A;Herzog K;Pflanz M;Wieland M;Rüger P;Kecke S;Kuhlmann H;Töpfer R
通讯作者：
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影响因子：
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DOI：
10.1109/tpami.2017.2699184
发表时间：
2018-04-01
影响因子：
23.6
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影响因子：
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影响因子：
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