论文标题
深层多视图图像融合，用于繁殖应用中大豆产量估计的深度多视图图像融合，用于繁殖应用中大豆产量估计的深度视图融合
Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications Deep Multi-view Image Fusion for Soybean Yield Estimation in Breeding Applications
论文作者
论文摘要
可靠的种子产量估计是植物育种计划的必不可少的一步，该计划旨在用于主要的行农作物中的品种开发。这项研究的目的是开发一种机器学习（ML）方法，在大豆[\ textIt {glycine max} L.（merr。）] pod计数中进行了计数，以启用基因型种子产量等级预测，从地面机器人收集的场地视频数据。为了实现这一目标，我们使用深度学习体系结构开发了一个基于图像的多视图估计框架。从不同角度捕获的植物图像被融合以估计产量，然后将大豆基因型对育种决策进行应用。我们使用了田间受控成像环境的数据，以及来自现场的植物育种测试地块，以通过将性能与手动POD计数和估算值进行比较来证明我们的框架的功效。 我们的结果表明，ML模型在做出繁殖决策时有望大大减少时间和人类的努力，并开放了新的育种方法来开发品种。
Reliable seed yield estimation is an indispensable step in plant breeding programs geared towards cultivar development in major row crops. The objective of this study is to develop a machine learning (ML) approach adept at soybean [\textit{Glycine max} L. (Merr.)] pod counting to enable genotype seed yield rank prediction from in-field video data collected by a ground robot. To meet this goal, we developed a multi-view image-based yield estimation framework utilizing deep learning architectures. Plant images captured from different angles were fused to estimate the yield and subsequently to rank soybean genotypes for application in breeding decisions. We used data from controlled imaging environment in field, as well as from plant breeding test plots in field to demonstrate the efficacy of our framework via comparing performance with manual pod counting and yield estimation. Our results demonstrate the promise of ML models in making breeding decisions with significant reduction of time and human effort, and opening new breeding methods avenues to develop cultivars.
