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Open AccessJournal ArticleDOI

Accurate inference of shoot biomass from high-throughput images of cereal plants.

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TLDR
A method based on plant specific weight for improving the accuracy of the linear model and reducing the estimation bias is proposed, which indicates that the proposed method can be used to estimate biomass of individual plants regardless of what variety the plant is and what salt treatment has been applied.
Abstract
With the establishment of advanced technology facilities for high throughput plant phenotyping, the problem of estimating plant biomass of individual plants from their two dimensional images is becoming increasingly important. The approach predominantly cited in literature is to estimate the biomass of a plant as a linear function of the projected shoot area of plants in the images. However, the estimation error from this model, which is solely a function of projected shoot area, is large, prohibiting accurate estimation of the biomass of plants, particularly for the salt-stressed plants. In this paper, we propose a method based on plant specific weight for improving the accuracy of the linear model and reducing the estimation bias (the difference between actual shoot dry weight and the value of the shoot dry weight estimated with a predictive model). For the proposed method in this study, we modeled the plant shoot dry weight as a function of plant area and plant age. The data used for developing our model and comparing the results with the linear model were collected from a completely randomized block design experiment. A total of 320 plants from two bread wheat varieties were grown in a supported hydroponics system in a greenhouse. The plants were exposed to two levels of hydroponic salt treatments (NaCl at 0 and 100 mM) for 6 weeks. Five harvests were carried out. Each time 64 randomly selected plants were imaged and then harvested to measure the shoot fresh weight and shoot dry weight. The results of statistical analysis showed that with our proposed method, most of the observed variance can be explained, and moreover only a small difference between actual and estimated shoot dry weight was obtained. The low estimation bias indicates that our proposed method can be used to estimate biomass of individual plants regardless of what variety the plant is and what salt treatment has been applied. We validated this model on an independent set of barley data. The technique presented in this paper may extend to other plants and types of stresses.

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Future scenarios for plant phenotyping.

TL;DR: An overview of the inherently multidisciplinary research in plant phenotyping is provided, focusing on traits that will assist in selecting genotypes with increased resource use efficiency and opportunities and challenges for integrating noninvasive or minimally invasive technologies into screening protocols.
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A review of imaging techniques for plant phenotyping.

TL;DR: A brief review on a variety of imaging methodologies used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress in plant phenotyping.
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Machine Learning for High-Throughput Stress Phenotyping in Plants

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TL;DR: This work highlights recent developments in high-throughput plant phenotyping using robotic-assisted imaging platforms and computer vision-assisted analysis tools.
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Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement

TL;DR: Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design.
References
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TL;DR: This paper provides an international methodological protocol aimed at standardising this research effort, based on consensus among a broad group of scientists in this field, and features a practical handbook with step-by-step recipes, for 28 functional traits recognised as critical for tackling large-scale ecological questions.
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction

TL;DR: This section will review those books whose content and level reflect the general editorial poltcy of Technometrics.
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