Image phenotyping of lettuce germplasm with genetically diverse carotenoid levels

Maciel, Gabriel Mascarenhas; Gallis, Rodrigo Bezerra de Araújo; Barbosa, Ricardo Luís; Pereira, Lucas Medeiros; Peixoto, Joicy Vitória Miranda

doi:10.1590/1678-4499.20190519

Cited by 8 publications

(4 citation statements)

References 39 publications

(56 reference statements)

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“…Current optimization approaches make use of crop features such as fresh weight and leaf shape index [29], dry biomass [30], carotenoid [31], stomatal conductance, size, and density [32], leaf starch content [24], root anatomy [33], and vitamin C and nitrate content [15]. Image phenotyping has been made possible through computer vision (CV) [5], [9].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Genetic Programming and Multiverse-based Optimization of Pre-Harvest Growth Factors of Aquaponic Lettuce Based on Chlorophyll Concentration

Concepcion¹,

Dadios²,

Cuello³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

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Optimizing photosynthesis is vital in maintaining quality farm produce in the agricultural food production sector. The nonlinear behavior of the interaction of crop pre-harvest growth factors can promote or retard its growth. This study employed multigene symbolic regression genetic programming (MSRGP) in developing the chlorophyll-a fitness function allied with bioinspired algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), and multiverse optimization (MVO), in determining the ideal combination of carbon dioxide, light intensity, air temperature, and humidity that will induce photosynthesis based on aquaponic lettuce (Lactuca sativa var. Altima) leaf chlorophyll-a concentration. Light spectra were characterized through the floating leaf disk technique, which resulted in white spectra as the most photosynthetic conducive based on the light reaction and dark respiration. Leaf spectro-textural-morphological signatures were extracted for non-destructive MSRGP chlorophyll-a concentration measurement. Carbon dioxide and humidity have a strong positive impact on chlorophyll-a concentration. Photosynthesis is impeded by Vis/IR above 7.817. The hybrid MSRGP-MVO generated the ideal global solution of 880.744 ppm of CO2, 543.147 μmol m -2 s -1 of the visible white light spectrum, 22.238 °C air temperature, and 67.742% humidity which resulted in 651.144 mg g -1 of Chl-a, 0.934 leaf weight ratio, 0.066 roots to shoot ratio, 141 xylem vessels mm -2 127.389 stomata mm -2 , and more prominent intracellular chloroplast concentration for the harvest stage lettuce. The established standard for fresh weight, chlorophylls a and b, and vitamin C concentrations are essential for developing an adaptive nutrient management system to maintain the expected growth signatures of lettuce at the end of the 6-week cultivation cycle.

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Section: Introductionmentioning

confidence: 99%

Hybrid Genetic Programming and Multiverse-based Optimization of Pre-Harvest Growth Factors of Aquaponic Lettuce Based on Chlorophyll Concentration

Concepcion¹,

Dadios²,

Cuello³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

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“…Maciel et al (2020) analyzed through univariate analysis the same germplasm bank featured in the present study. These authors observed the genotypes selected by the Mulamba & Mock and Subandi indexes are among those that showed the best agronomic and nutritional characteristics.…”

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confidence: 99%

Comparison between non-parametric indexes in the selection of biofortified curly lettuce

et al. 2020

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Selection indexes can be applied to simultaneous analysis of traits, increasing the efficiency of breeding in choosing the genotypes that meet the needs of both the market and the consumer. While it is a technic applied to several cultures, there are few studies about it in lettuce lines. For this reason, the aim of this study was to comparate assess the efficiency of different selection indexes for selecting biofortifiedleaf lettuce lines with good agronomic traits. The experiment was carried out at the Estação Experimental de Hortaliças of Federal University of Uberlândia (UFU), campus Monte Carmelo, in 2018. The experiment was designed in full randomized blocks of 3 repetitions with 25 leaf lettuce genotypes (22 lines from cultivars Pira 72 versus Uberlândia 10000 and 3 commercial cultivars – cv. Grand Rapids, UFU-Biofort and Uberlândia 10000). The lines are part of the UFU biofortified lettuce breeding program and were assessed for the total green mass (g), the stem diameter (cm), commercial leaves count, plant diameter (cm), foliar temperature (°C), SPAD index, anthocyanin content (mg 100g-1 sample), and bolting (days after sowing). To estimate selection gains, 10 genotypes were selected using both direct and indirect selection, as well as the traditional index, the sum of ranks index, Willians base index, and the multiplicative index. Mulamba and Mock sum of ranks index and Subandi multiplicative index provided the biggest total gains for the traits assessed in biofortified leaf lettuce. Both indexes were similar in selecting genotypes.

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“…Image-based phenotyping can provide an automated, non-destructive, and cost-effective method for quantifying lettuce plant/leaf traits. At the plant population level, unmanned aerial vehicle (UAV) has been used to evaluate the genetic diversity in red leaf lettuce germplasm [ 3 ], identify agronomic characteristics and carotenoid-rich genotypes [ 4 ], and measure yield-related phenotypes from ultra-large aerial imagery [ 5 ]. At the plant level, high-resolution images of individual lettuces can be used to extract and evaluate more complex traits, such as aboveground biomass [ 6 ], leaf canopy area, color, texture [ 7 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative phenotyping and evaluation for lettuce leaves of multiple semantic components

et al. 2022

Plant Methods

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Background Classification and phenotype identification of lettuce leaves urgently require fine quantification of their multi-semantic traits. Different components of lettuce leaves undertake specific physiological functions and can be quantitatively described and interpreted using their observable properties. In particular, petiole and veins determine mechanical support and material transport performance of leaves, while other components may be closely related to photosynthesis. Currently, lettuce leaf phenotyping does not accurately differentiate leaf components, and there is no comparative evaluation for positive-back of the same lettuce leaf. In addition, a few traits of leaf components can be measured manually, but it is time-consuming, laborious, and inaccurate. Although several studies have been on image-based phenotyping of leaves, there is still a lack of robust methods to extract and validate multi-semantic traits of large-scale lettuce leaves automatically. Results In this study, we developed an automated phenotyping pipeline to recognize the components of detached lettuce leaves and calculate multi-semantic traits for phenotype identification. Six semantic segmentation models were constructed to extract leaf components from visible images of lettuce leaves. And then, the leaf normalization technique was used to rotate and scale different leaf sizes to the “size-free” space for consistent leaf phenotyping. A novel lamina-based approach was also utilized to determine the petiole, first-order vein, and second-order veins. The proposed pipeline contributed 30 geometry-, 20 venation-, and 216 color-based traits to characterize each lettuce leaf. Eleven manually measured traits were evaluated and demonstrated high correlations with computation results. Further, positive-back images of leaves were used to verify the accuracy of the proposed method and evaluate the trait differences. Conclusions The proposed method lays an effective strategy for quantitative analysis of detached lettuce leaves' fine structure and components. Geometry, color, and vein traits of lettuce leaf and its components can be comprehensively utilized for phenotype identification and breeding of lettuce. This study provides valuable perspectives for developing automated high-throughput phenotyping application of lettuce leaves and the improvement of agronomic traits such as effective photosynthetic area and vein configuration.

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Image phenotyping of lettuce germplasm with genetically diverse carotenoid levels

Cited by 8 publications

References 39 publications

Hybrid Genetic Programming and Multiverse-based Optimization of Pre-Harvest Growth Factors of Aquaponic Lettuce Based on Chlorophyll Concentration

Hybrid Genetic Programming and Multiverse-based Optimization of Pre-Harvest Growth Factors of Aquaponic Lettuce Based on Chlorophyll Concentration

Comparison between non-parametric indexes in the selection of biofortified curly lettuce

Quantitative phenotyping and evaluation for lettuce leaves of multiple semantic components

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