较近来自Hiphen公司、法国农业科学院、Arvalis植物研究的科学家在先进期刊Plant Phenomics植物表型组学上发表了题为Scoring Cercospora Leaf Spot on Sugar Beet: Comparison of UGV and UAV Phenotyping Systems的文章，就UGV和UAV表型成像系统在甜菜叶斑病的评估进行了比较

Hiphen是专注于在植物育种、有效农业领域提供高通量表型系统和服务的公司，公司与 ARVALIS植物研究所、INRA法国农业科学院进行联合科研，开发出了相关系统和方法。Hiphen公司也向植物育种领域的育种公司、作物生产团体以及其它客户提供技术服务。自2014年成立以来，Hiphen以其先进的团队以及研究技术屡获业界大奖和殊荣，其先进的产品和服务已经获得先进客户认可，Hiphen致力于成为第四次农业革命领域的领军企业，其合作伙伴广泛分布于各地，在遥感领域，刚刚和Planet达成战略合作，深耕农业领域。Hiphen公司的特色为云生物技术，集云计算、大数据分析和植物生物学为一体，能够帮助各大科研院所、各大公司改良作物，不仅所需时间短，而且成本相对低。其开发的名为 Cloverfield 的机器学习平台，使用机器学习技术来较准确地预测植物特性。该平台主要使用机器学习进行基因功能和表达的预测。该公司已成功地与种子、食品和配料公司建立了伙伴关系。Hiphen 还开发出了系列田间表型成像产品，主要进行针对农作物的近视距田间数据收集，开发了一款名为 Phenomobiles的机器人，它使用多光谱、激光雷达和RGB相机等传感器来收集植物特征数据，如植物茎宽、植物高度、器官数量、植株数量、数十种植被指数，该公司的还致力于利用计算机视觉分析田间数据，收集有关作物量、叶绿素含量、植物高度等信息，研究中还用到神经网络来帮助减少误报。

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Scoring Cercospora Leaf Spot on Sugar Beet: Comparison of UGV and UAV Phenotyping Systems 

S. Jay,¹ A. Comar,² R. Benicio,² J. Beauvois,² D. Dutartre,² G. Daubige,¹ W. Li,2 J. Labrosse,2 S. Thomas,3 N. Henry,4 M. Weiss,1 and F. Baret1

Abstract

Selection of sugar beet (Beta vulgaris L.) cultivars that are resistant to Cercospora Leaf Spot (CLS) disease is critical to increase yield. Such selection requires an automatic, fast, and objective method to assess CLS severity on thousands of cultivars in the field. For this purpose, we compare the use of submillimeter scale RGB imagery acquired from an Unmanned Ground Vehicle (UGV) under active illumination and centimeter scale multispectral imagery acquired from an Unmanned Aerial Vehicle (UAV) under passive illumination. Several variables are extracted from the images (spot density and spot size for UGV, green fraction for UGV and UAV) and related to visual scores assessed by an expert. Results show that spot density and green fraction are critical variables to assess low and high CLS severities, respectively, which emphasizes the importance of having submillimeter images to early detect CLS in field conditions. Genotype sensitivity to CLS can then be accurately retrieved based on time integrals of UGV- and UAV-derived scores. While UGV shows the best estimation performance, UAV can show accurate estimates of cultivar sensitivity if the data are properly acquired. Advantages and limitations of UGV, UAV, and visual scoring methods are finally discussed in the perspective of high-throughput phenotyping.

http://spj.sciencemag.org/journals/plantphenomics/2020/9452123/