An Automated Field Phenotyping Pipeline for Application in Grapevine Research

Kicherer, Anna; Herzog, Katja; Pflanz, Michael; Wieland, Markus; Rüger, Philipp; Kecke, Steffen; Kuhlmann, Heiner; Töpfer, Reinhard

doi:10.3390/s150304823

Cited by 57 publications

(62 citation statements)

References 29 publications

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“…Automated platforms are under development in different fields of remote sensing to overcome this difficulty but they have not yet become standard. Kicherer et al [78] developed a robotic platform for phenotyping grapevine based on automatic image acquisition. Results of a mobile multi-sensor phenotyping platform for phenotyping of winter wheat are presented by Kipp et al [79].…”

Section: Biomass Estimation From Vegetation Indicesmentioning

confidence: 99%

Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

2015

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Plant biomass is an important parameter for crop management and yield estimation. However, since biomass cannot be determined non-destructively, other plant parameters are used for estimations. In this study, plant height and hyperspectral data were used for barley biomass estimations with bivariate and multivariate models. During three consecutive growing seasons a terrestrial laser scanner was used to establish crop surface models for a pixel-wise calculation of plant height and manual measurements of plant height confirmed the results (R 2 up to 0.98). Hyperspectral reflectance measurements were conducted with a field spectrometer and used for calculating six vegetation indices (VIs), which have been found to be related to biomass and LAI: GnyLi, NDVI, NRI, RDVI, REIP, and RGBVI. Furthermore, biomass samples were destructively taken on almost the same dates. Linear and exponential biomass regression models (BRMs) were established for evaluating plant height and VIs as estimators of fresh and dry biomass. Each BRM was established for the whole observed period and pre-anthesis, which is important for management decisions. Bivariate BRMs supported plant height as a strong estimator (R 2 up to 0.85), whereas BRMs based on individual VIs showed varying performances (R 2 : 0.07-0.87). Fused approaches, where plant height and one VI were used for establishing multivariate BRMs, yielded improvements in some cases (R 2 up to 0.89). Overall, this study reveals the potential of remotely-sensed plant parameters for estimations of barley biomass. Moreover, it is a first step towards the fusion of 3D spatial and spectral measurements for improving non-destructive biomass estimations. OPEN ACCESSRemote Sens. 2015, 7 11450

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Section: Biomass Estimation From Vegetation Indicesmentioning

confidence: 99%

Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

2015

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“…For example, thermal remote sensing is used to estimate evaporation and drought stress agriculture (Maes and Steppe, 2012) and automatic phenotyping pipelines have been developed to monitor the color of grape berries in the field (Kicherer et al, 2015). In addition, the coverage of field monitoring can be increased by aerial phenotyping and multi-sensor approaches (Virlet et al, 2014;Liebisch et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

A Journey Through a Leaf: Phenomics Analysis of Leaf Growth inArabidopsis thaliana

Vanhaeren

González

Inzé

2015

The Arabidopsis Book

136

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In Arabidopsis, leaves contribute to the largest part of the aboveground biomass. In these organs, light is captured and converted into chemical energy, which plants use to grow and complete their life cycle. Leaves emerge as a small pool of cells at the vegetative shoot apical meristem and develop into planar, complex organs through different interconnected cellular events. Over the last decade, numerous phenotyping techniques have been developed to visualize and quantify leaf size and growth, leading to the identification of numerous genes that contribute to the final size of leaves. In this review, we will start at the Arabidopsis rosette level and gradually zoom in from a macroscopic view on leaf growth to a microscopic and molecular view. Along this journey, we describe different techniques that have been key to identify important events during leaf development and discuss approaches that will further help unraveling the complex cellular and molecular mechanisms that underlie leaf growth.

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“…Leading image processing techniques strive to efficiently extract accurate and reliable high level information from this data, such as the crop health, maturity, distribution etc. Image classification and segmentation techniques are often used in orchard data for fruit detection and yield estimation [1]- [5], crop quality assessment [6] and trunk detection for tree mapping [7]. Such information enables precision farming, where processes such as pesticide spraying and fertilisation are modified according to in-field variations, ultimately leading to maximizing yield and quality while minimising costs.…”

Section: Introductionmentioning

confidence: 99%

Image classification with orchard metadata

Bargoti

Underwood

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Low cost and easy to use monocular vision systems are able to capture large scale, dense data in orchards, to facilitate precision agriculture applications. Accurate image parsing is required for this purpose, however, operating in natural outdoor conditions makes this a complex task due to the undesirable intra-class variations caused by changes in illumination, pose and tree types, etc. Typically these variations are difficult to explicitly model and discriminative classifiers strive to be invariant to them. However, given the presence of structure, in both the orchard and how the data was obtained, a subset of these factors of variations can correlate with readily available metadata, including extrinsic experimental information such as the sun incidence angle, position within farm, etc. This paper presents a method to incorporate such metadata to aid scene parsing based on a multi-scale Multi-Layered Perceptron (MLP) architecture. Experimental results are shown for pixel segmentation over data collected at an apple orchard, leading to fruit detection and yield estimation. The results show a consistent improvement in segmentation accuracy with the inclusion of metadata under different network complexities, training configurations and evaluation metrics.

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An Automated Field Phenotyping Pipeline for Application in Grapevine Research

Cited by 57 publications

References 29 publications

Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

Fusion of Plant Height and Vegetation Indices for the Estimation of Barley Biomass

A Journey Through a Leaf: Phenomics Analysis of Leaf Growth inArabidopsis thaliana

Image classification with orchard metadata

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