Applications of High-Throughput Phenotypic Phenomics

Ahmed, Hafiz Ghulam Muhu-Din; Zeng, Yawen; Fiaz, Sajid; Rashid, Abdul

doi:10.1007/978-3-031-15568-0_6

Cited by 2 publications

(2 citation statements)

References 55 publications

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“…Over the last decades, efforts have focused on the development of high-throughput phenotyping methods to overcome these constraints [24][25][26]; one potentially robust approach relies on using image analysis to measure and analyze plant health [27][28][29][30]. This simple and low-cost solution mostly uses red-green-blue (RGB) imagery to extract information about the shape, texture, and color of plants, and has shown promise for the evaluation of different abiotic and biotic stresses even before the typical symptoms manifest [31,32].…”

Section: Introductionmentioning

confidence: 99%

The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch

Leiva,

Dhakal,

Himanen

et al. 2024

Plants

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Challenges of climate change and growth population are exacerbated by noticeable environmental changes, which can increase the range of plant diseases, for instance, net blotch (NB), a foliar disease which significantly decreases barley (Hordeum vulgare L.) grain yield and quality. A resistant germplasm is usually identified through visual observation and the scoring of disease symptoms; however, this is subjective and time-consuming. Thus, automated, non-destructive, and low-cost disease-scoring approaches are highly relevant to barley breeding. This study presents a novel screening method for evaluating NB severity in barley. The proposed method uses an automated RGB imaging system, together with machine learning, to evaluate different symptoms and the severity of NB. The study was performed on three barley cultivars with distinct levels of resistance to NB (resistant, moderately resistant, and susceptible). The tested approach showed mean precision of 99% for various categories of NB severity (chlorotic, necrotic, and fungal lesions, along with leaf tip necrosis). The results demonstrate that the proposed method could be effective in assessing NB from barley leaves and specifying the level of NB severity; this type of information could be pivotal to precise selection for NB resistance in barley breeding.

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Section: Introductionmentioning

confidence: 99%

The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch

Leiva,

Dhakal,

Himanen

et al. 2024

Plants

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“…Despite PA’s contributions to sustainable agriculture, its use in resource-constrained smallholder farming environments, particularly in Sub-Saharan Africa (SSA), has been very limited ( Gobezie and Biswas, 2023 ). Recent developments in sensor technologies, machine vision, and higher-resolution digital cameras, in tandem with advanced data processing power and other portable tools have paved the way for high-throughput plant phenotyping in the field to benefit crop breeding programs ( Deery et al., 2014 ; Zhang et al., 2016 ; Araus et al., 2022 ; Ahmed et al., 2023 ). From the field phenotyping perspective, these emerging technologies are enabling automated intensive data collection and increasing the ability to investigate plant function and structure through non-invasive methods with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Field phenotyping for African crops: overview and perspectives

Cudjoe,

Virlet,

Castle

et al. 2023

Front. Plant Sci.

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Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.

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Applications of High-Throughput Phenotypic Phenomics

Cited by 2 publications

References 55 publications

The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch

The Combination of Low-Cost, Red–Green–Blue (RGB) Image Analysis and Machine Learning to Screen for Barley Plant Resistance to Net Blotch

Field phenotyping for African crops: overview and perspectives

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