Dow AgroSciences LLC, 9330 Zionsville Rd, Indianapolis, 46268, IN, USA.
BMC Bioinformatics. 2018 Aug 13;19(Suppl 9):289. doi: 10.1186/s12859-018-2267-2.
Maize is a leading crop in the modern agricultural industry that accounts for more than 40% grain production worldwide. THe double haploid technique that uses fewer breeding generations for generating a maize line has accelerated the pace of development of superior commercial seed varieties and has been transforming the agricultural industry. In this technique the chromosomes of the haploid seeds are doubled and taken forward in the process while the diploids marked for elimination. Traditionally, selective visual expression of a molecular marker within the embryo region of a maize seed has been used to manually discriminate diploids from haploids. Large scale production of inbred maize lines within the agricultural industry would benefit from the development of computer vision methods for this discriminatory task. However the variability in the phenotypic expression of the molecular marker system and the heterogeneity arising out of the maize genotypes and image acquisition have been an enduring challenge towards such efforts.
In this work, we propose a novel application of a deep convolutional network (DeepSort) for the sorting of haploid seeds in these realistic settings. Our proposed approach outperforms existing state-of-the-art machine learning classifiers that uses features based on color, texture and morphology. We demonstrate the network derives features that can discriminate the embryo regions using the activations of the neurons in the convolutional layers. Our experiments with different architectures show that the performance decreases with the decrease in the depth of the layers.
Our proposed method DeepSort based on the convolutional network is robust to the variation in the phenotypic expression, shape of the corn seeds, and the embryo pose with respect to the camera. In the era of modern digital agriculture, deep learning and convolutional networks will continue to play an important role in advancing research and product development within the agricultural industry.
玉米是现代农业的主要作物，占全球粮食产量的 40%以上。利用较少的育种世代来产生玉米品系的双单倍体技术加速了优良商业种子品种的发展步伐，正在改变农业产业。在该技术中，加倍单倍体种子的染色体，并在过程中继续进行，而将标记为消除的二倍体。传统上，通过选择性地表达玉米种子胚区的分子标记，手动区分二倍体和单倍体。农业产业中大量生产自交系玉米品系将受益于开发用于该鉴别任务的计算机视觉方法。然而，分子标记系统的表型表达的可变性以及玉米基因型和图像采集产生的异质性一直是此类努力的持久挑战。
在这项工作中，我们提出了一种将深度卷积网络（DeepSort）应用于这些现实环境中单倍体种子分类的新方法。我们提出的方法优于基于颜色、纹理和形态的特征的现有最先进的机器学习分类器。我们证明了该网络可以通过卷积层中的神经元的激活来区分胚区的特征。我们对不同架构的实验表明，随着层深度的降低，性能会下降。
我们提出的基于卷积网络的 DeepSort 方法对表型表达、玉米种子形状以及相对于相机的胚体姿势的变化具有鲁棒性。在现代数字农业时代，深度学习和卷积网络将继续在农业产业的研究和产品开发中发挥重要作用。
