Advances in High-Throughput Phenotyping of Maize (Zea Mays L.) for Climate Resilience

Basavaraj, P. S.; Rane, Jagadish; Prathibha, M. D.; Boraiah, K. M.; Kumar, Mahesh

doi:10.1007/978-3-031-21640-4_12

Cited by 2 publications

(1 citation statement)

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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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