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Image phenotyping of inbred red lettuce lines with genetic diversity regarding carotenoid levels
Int J Appl Earth Obs Geoinformation 81 (2019) 154–160
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Int J Appl Earth Obs Geoinformation journal homepage: www.elsevier.com/locate/jag
Image phenotyping of inbred red lettuce lines with genetic diversity regarding carotenoid levels
T
Gabriel Mascarenhas Maciel, Rodrigo Bezerra de Araújo Gallis, Ricardo Luís Barbosa, ⁎ Lucas Medeiros Pereira, Ana Carolina Silva Siquieroli, Joicy Vitória Miranda Peixoto Universidade Federal de Uberlândia, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Geotechnology Biofortification Germplasm bank Genetic dissimilarity Unmanned aerial vehicle (UAV)
Developing biofortified foods is a goal of genetic breeding programs. However, analysis costs and the time required for leaf sampling in the field are hindrances to this process. The objectives of this study were to evaluate the genetic diversity in red leaf lettuce germplasm and to evaluate the use of image phenotyping for the identification of carotenoid-rich genotypes. The experiment was carried out in 2018 at the Vegetable Experiment Station of the Federal University of Uberlândia-Monte Carmelo campus. Thirty inbred lines of red leaf lettuce were evaluated. All inbred lines resulted from the hybridization of the Belíssima and Uberlândia 10000 cultivars and six successive selfings, carried out from 2013 to 2017. The genealogical method or pedigree is a working procedure used by plant breeders working with plant species whose reproductive system is autogamous and presenting cleistogamy. This method was used to obtain the treatments of the experiment. The cultivar Belíssima (red lettuce) was used as a control, totaling 31 treatments. A conventional method and an aerial image phenotyping using Phantom 4 unmanned aerial vehicle (UAV) were used to evaluate six different agronomic characteristics and carotenoid levels of each treatment. The results showed substantial genetic diversity within the germplasm bank. Furthermore, high performance image phenotyping was highly correlated with the traditional methodology (r = -0.8732, coefficient of determination = 76.25%) and can therefore be considered an alternative for identifying different genetic backgrounds within a germplasm bank. Unmanned aerial vehicle (UAV) might also be used to monitor biofortification levels in crops.
1. Introduction Lettuce (Lactuca sativa L.) comes in various shapes and colors that are represented by four different categories (crispy, smooth, romaine and crisphead) (Sala and da Costa, 2012). Current lettuce genetic diversity allows farmers to select from various options. In Brazil, leaf lettuce is more popular than crisphead, smooth and romaine lettuce (Sala et al., 2008; Sala and da Costa, 2012). Among the various leaf lettuces grown in Brazil, 2360 ha are dedicated to red lettuce (ABCSEM, 2016). The cultivation of red leaf lettuce has allowed growers to add value and reach specific market niches. In addition to being visually attractive, the consumption of red lettuce can help prevent several diseases related to oxidative stress (Maiani et al., 2009; Rocha and Reed, 2014) due to the presence of carotenoids, which are precursors to vitamin A (Sousa et al., 2007; Silva and Mura, 2010; Cassetari et al., 2015). Despite these qualitative and commercial advantages, there are currently no red leaf cultivars that are rich in carotenoids. Sousa et al. (2007) ⁎
identified a green, smooth-leaved cultivar that is rich in carotenoids (Uberlândia 10,000). However, few studies have sought to obtain carotenoid-rich lettuce cultivars. One probable reason may be related to the high cost of analyzing leaf constituents and the time needed to carry out laboratory analyses. Some studies have demonstrated more economical and efficient methodologies that can benefit breeding programs. Cassetari et al. (2015) suggested that the SPAD index could be used to indirectly quantify carotenoids from leaf chlorophyll content. However, other methodologies exist that could revolutionize the phenotyping of plants with different carotenoid levels. One new alternative is image phenotyping, which has already been used to select various types of vegetables (De Sousa et al., 2015), but not lettuce. Image phenotyping can be used to determine qualitative and/or quantitative values and correlate these with genotype performance within a given environment (Dhondt et al., 2013). Plant phenotyping offers non-destructive optical analyses of plant characteristics, mainly via images (Walter et al., 2015). Current techniques employ a
Corresponding author. E-mail address: [email protected] (J. Vitória Miranda Peixoto).
https://doi.org/10.1016/j.jag.2019.05.016 Received 3 January 2019; Received in revised form 10 May 2019; Accepted 13 May 2019 Available online 23 May 2019 0303-2434/ © 2019 Elsevier B.V. All rights reserved.
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centimeters. Leaf temperature was determined by aiming an infrared thermometer (model 4000.4 G L, Everest Interscience, Tucson, AZ, USA) at the center of an upper leaf. The SPAD/carotenoid index (Cassetari et al., 2015) was calculated as the mean of the index from the four central plants of each plot. The reading was made with a chlorophyll meter (Minolta SPAD-502 CFL1030) in triplicate, in both lateral sheets, opposite each other and in the intermediate sheet of each plant. The number of days from sowing to bolting (DBS) was also recorded. The data were submitted to analysis of variance (F test, p ≤ 0.05) and the means compared by the Scott-Knott test (p < 0.05). Then, multivariate analyses of genetic dissimilarity between genotypes were determined using the Mahalanobis generalized distance (D2ii´ ). Genetic divergence was represented by a dendrogram that was obtained using the hierarchical Unweighted Pair-Group Method Using Arithmetic Averages (UPGMA) and graphical Tocher optimization. UPGMA clustering was evaluated using a cophenetic correlation coefficient (CCC) calculated by the Mantel (1967) test. The relative contribution of quantitative traits was calculated according to Singh (1981). All data were analyzed using Genes version 2015.5.0 software (Cruz, 2013). In addition to using a conventional methodology based on the SPAD/carotenoid index (Cassetari et al., 2015), image phenotyping of the same genotypes was performed using aerial images taken with a RGB camera with CMOS sensor and 9 mm of focal lenght mounted on a Phantom 4 unmanned aerial vehicle (UAV). The images were collected with 4864 × 3648 pixels and stored in JPEG format. The flight parameters were: 20-meter height, 80% longitudinal overlap and 75% lateral overlap. The flight was performed automatically using proprietary DroneDeploy software. The resulting images were used to generate an orthophoto with a GSD (Ground Sample Distance) of 1 cm (Pix4d software). The heat map was generated from the georeferencing of the four plants of the useful area by means of an orthophoto. Then, using the average value obtained by SPAD, kriging was performed with the objective of extrapolating the result observed in the useful area to the total plot area. Histograms were generated for the two most contrasting lines in SPAD/carotenoid index. Each plot was manually delimited in the orthophoto and ImageJ v. 1.52 software was used to isolate the green channel RGB image. Next, mean green intensity values (G) were calculated for each plot and then correlated with SPAD values using SIGMA PLOT software (5% significance). The software QGis v. 3.0 was used in addition to the correlation, to visualize the geo-spatial distribution of the SPAD/carotenoid index with the heat map generated using the IDW (Inverse Distance Weighted) function as a kernel.
multidisciplinary approach that includes spectroscopy and image generation (De Sousa et al., 2015). These images can be easily collected using unmanned aerial vehicle (UAV), which are also known as drones. These aircraft can carry various types of cameras with sensors that capture images ranging from visible electromagnetic to infrared that can be used in various vegetation analyses (De Sousa et al., 2015; Yang et al., 2017). The resulting combination of images with high spatial and temporal resolution is one of the main attractions for using this technology in lettuce phenotyping. Current studies have attempted to correlate vegetation reflectance with the characteristics of several plant species (Antunes et al. 2012; Johann et al., 2012; Risso et al., 2012; Santi et al., 2012; De C Victoria et al., 2012; Picoli et al., 2013; Vicente et al., 2012; Makanza et al., 2018), but not vegetables. The use of imaging techniques to aid in the indirect selection of carotenoid-rich lettuce genotypes is not yet a reality. Therefore, the objective of this study is to determine genetic diversity in germplasm of red leaf lettuce and to validate the use of image phenotyping in identifying carotenoid-rich genotypes. 2. Material and methods The experiment was carried out in 2018 at the Vegetable Experiment Station of the Federal University of Uberlândia - Monte Carmelo campus (18°42′43.19″ S, 47°29′55.8″ W, 873 m altitude), which is part of the UFU’s Breeding Program for Biofortified and Tropicalized Lettuce. The study evaluated 30 inbred lines of red leaf lettuce that were hybridized from the Belíssima cultivar and the carotenoid-rich Uberlândia 10,000 cultivar (Sousa et al., 2007) and six successive selfings carried out between 2013 and 2017. The genealogical method or pedigree is a working procedure used by plant breeders working with plant species whose reproductive system is autogamous and presenting cleistogamy. This method was used to obtain the treatments of the experiment. The cultivar Belíssima (red lettuce), widely cultivated in Brazil, was used as a control. This cultivar together with 30 more red leaf lettuce genotypes totaled the 31 treatments evaluated in the experiment. The pedigree method is based on individual plant selection in the segregating population. Evaluation was performed on each individual progeny. The superiority of the selected individuals was analyzed by the progeny test. The selection with progeny testing and knowledge of the genealogy of the chosen individuals allow the maximization of selection efficiency. The screening was carried out based on the genotypes of individuals as per Borém et al. (2017) method. Sowing took place on March 28, 2018. The seedlings were produced in 200-cell expanded polystyrene trays filled with a commerciallyavailable coconut fiber substrate. After sowing, the trays were maintained in a hoop-style greenhouse (area: 5 m x 6 m, height: 3.5 m), covered with UV resistant polyethylene film (150 microns) and antiaphid side screens. At 29 days after sowing, the seedlings were transplanted to field beds that had been prepared with a rototiller-hiller (1.3 m wide). Before setting up the experiment, soil samples were taken from a depth of 0 cm–20 cm and analyzed at the Soil Fertility Laboratory of the Federal University of Uberlândia. The physical/chemical analysis showed the following: clayey texture (> 50%), pH in CaCl2 = 4.9, SOM = 3.9 dag kg−1, P(rem) =79.1 mg dm-3, K = 0.29 cmolc dm-3, Ca = 3.3 cmolc dm-3, Mg = 1.3 cmolc dm-3, H + Al = 4.9 cmolc dm-3, SB = 4.90 cmolc dm-3, CEC = 9.80 cmolc dm-3, BS% = 50. Crop treatments were carried out as recommended for lettuce (Filgueira, 2013). Each experimental plot (Fig. 3) consisted of 16 plants in four rows (spaced 0.25 m x 0.25 m), from which the eight centermost plants were evaluated. The plants were harvested 35 days after transplant and taken to a laboratory where total fresh weight was determined by weighing all outer leaves. Stem diameter was measured with calipers. Numbers of commercially viable leaves were determined by counting leaves over 5 cm in length. Plant diameter was measured and expressed in
3. Results and discussion 3.1. Agronomic evaluation of germplasm During the evaluation period (September – November) the maximum temperatures varied from 36.5 °C to 19.5 °C (mean =30 °C) while the minimum temperatures varied from 22.7 °C to 11.8 °C (mean =17.6 °C), (SISMET, 2018). These temperatures were above the optimal range for lettuce cultivation, which is 4 °C to 27 °C (Do Santos et al., 2009). Significant differences (F test, p < 0.05) were found among the lettuce inbred lines for all variables except stem diameter and leaf temperature (Table 1). The fresh weight values of UFU-199#6#1#1, UFU-75#1#3#1, UFU-199#2#3#1 and UFU-184#2#1#1 differed most significantly from the other inbred lines, including the control, cv Belíssima (Table 1). The fresh weight of the most exceptional lines in the present study were greater than those found for the red leaf cultivars Pira Roxa and Belíssima (Blat et al., 2011), and similar to values found in other studies (Diamante et al., 2013; Suinaga et al., 2013; Aquino et al., 2014; 155
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Table 1 Fresh weight (FW), stem diameter (SD), leaf number (LN), plant diameter (PD), leaf temperature (LT), days to bolting after sowing (DBS) and the SPAD/carotenoid index of various inbred lines of leaf lettuce (SPAD). Treatments
FW (g)
UFU-199#2#2#1 UFU-7#2#1#1 UFU-117#1#3#1 UFU-86#1#2#1 UFU-75#1#1#1 UFU-199#6#1#1 UFU-206#3#2#1 UFU-199#2#3#1 UFU-189#2#3#1 UFU-184#2#5#1 UFU-184#2#1#1 UFU-107#1#2#1 UFU-86#2#1#1 UFU-75#3#2#1 UFU-184#2#3#1 UFU-199#6#2#1 UFU-7#1#1#1 UFU-75#2#2#1 UFU-75#3#1#1 UFU-190#1#2#1 UFU-75#1#3#1 UFU-189#2#2#1 UFU-189#2#1#1 UFU-199#2#1#1 UFU-199#5#1#1 UFU-206#1#6#1 UFU-206#1#3#1 UFU-117#1#1#1 UFU-184#2#1 UFU-199#1#1#1 cv. Belíssima Overall average CV(%)
88.58 b 38.33 e 44.42 e 56.25 d 63.67 c 120.33 a 100.75 b 116.75 a 30.25 f 42.83 e 116.67 a 49.58 d 40.83 e 31.33 f 55.58 d 66.00 c 44.08 e 70.58 c 63.00 c 27.67 f 117.58 a 38.25 e 41.08 e 89.08 b 52.42 d 49.08 d 53.50 d 24.67 f 69.75 c 74.92 c 23.25 f 61.33 14.42
SD (cm)
1.7 a 1.5 a 1.3 a 1.7 a 1.1 a 1.9 a 1.6 a 2.0 a 1.4 a 1.7 a 1.7 a 1.6 a 1.8 a 1.2 a 1.9 a 1.8 a 1.8 a 1.5 a 1.7 a 1.3 a 1.7 a 1.2 a 1.6 a 1.1 a 2.3 a 1.9 a 1.1 a 1.3 a 1.9 a 1.2 a 1.3 a 1.6 40.29
LN
16.7 a 15.9 b 17.8 a 14.8 b 17.7 a 18.1 a 12.0 b 14.8 b 16.1 b 19.9 a 19.9 a 17.8 a 15.5 b 10.8 b 20.5 a 15.2 b 20.1 a 13.3 b 12.4 b 14.3 b 22.6 a 13.1 b 17.7 a 15.3 b 18.7 a 15.8 b 14.5 b 12.2 b 19.8 a 15.1 b 8.3 b 16.0 16.78
PD (cm)
LT (ºC)
DBS (days)
Index SPAD/Carotenoid
RS1 (%)
23.3 a 20.8 a 21.5 a 23.2 a 23.1 a 27.2 a 18.0 b 21.3 a 20.6 a 21.0 a 25.6 a 19.3 b 20.4 a 18.5 b 26.0 a 22.2 a 22.3 a 21.2 a 21.3 a 15.3 b 24.5 a 15.0 b 21.0 a 21.9 a 18.7 b 22.4 a 19.3 b 16.0 b 24.6 a 24.4 a 10.9 b 21.0 17.13
25.0 a 24.2 a 24.5 a 23.1 a 24.6 a 24.3 a 24.4 a 23.6 a 25.1 a 24.4 a 24.0 a 25.0 a 24.5 a 25.1 a 24.6 a 23.9 a 23.7 a 23.9 a 23.5 a 24.9 a 23.3 a 25.1 a 26.0 a 24.1 a 24.5 a 24.7 a 26.1 a 25.2 a 23.7 a 23.9 a 24.1 a 24.4 6.37
81.0 c 93.3 b 84.0 c 85.3 c 84.0 c 83.3 c 77.0 c 83.3 c 83.7 c 98.7 b 83.0 c 87.0 c 90.7 c 82.3 c 92.7 b 86.0 c 100.3 a 85.7 c 80.7 c 105.3 a 96.7 b 88.0 c 91.7 b 81.0 c 108.0 a 89.0 c 81.0 c 85.3 c 94.3 b 86.7 c 86.7 c 88.2 4.87
38.9 b 29.2 e 23.5 f 24.4 f 34.7 c 38.5 b 29.6 e 28.3 e 28.1 e 37.7 b 27.0 e 33.6 c 30.4 d 31.4 d 32.1 d 41.9 a 31.6 d 42.7 a 31.4 d 26.6 e 34.3 c 29.2 e 34.6 c 42.9 a 37.9 b 28.9 e 41.4 a 18.6 g 35.5 c 44.7 a 17.6 g 32.5 5.58
121.0 65.9 33.5 38.6 97.2 118.8 68.2 60.8 59.7 114.2 53.4 90.9 72.7 78.4 82.4 138.1 79.5 142.6 78.4 51.1 94.9 65.9 96.6 143.8 115.3 64.2 135.2 5.7 101.7 154.0 0.0
Means followed by distinct letters within a column differ by the Scott-Knott test (0.05 significance). 1(RS) Relative superiority of the SPAD/carotenoid index of lettuce inbred lines compared to the control, cv. Belíssima.
Fig. 1. Dendrogram of genetic divergence among 30 lettuce inbred lines and a commercial control (cv. Belíssima) - obtained using the hierarchical Unweighted PairGroup Method (UPGMA) and dissimilarity measures.
numbers that were significantly greater than cv. Belíssima (Scott-knott, p ≤ 0.05) (Table 1). Similar leaf numbers have been found by other authors (Do Santos et al., 2009; Diamante et al., 2013; Blat et al., 2011; Aquino et al., 2014). To date, there has been limited research on the agronomic performance of red leaf lettuce cultivars. However, in general, the agronomic
Ziech et al., 2014; Brzezinski et al., 2017), suggesting that these red leaf lettuces meet the standards of commercial cultivars. UFU-199#2#2#1, UFU-117#1#3#1, UFU-75#1#1#1, UFU199#6#1#1, UFU-184#2#5#1, UFU-184#2#1#1, UFU-107#1#2#1, UFU-184#2#3#1, UFU-7#1#1#1, UFU-75#1#3#1, UFU189#2#1#1, UFU-199#5#1#1 and UFU-184#2#1 produced leaf 156
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Fig. 2. Clustering by graphical Tocher optimization, based on seven agronomic characteristics of 31 red leaf lettuce inbred lines. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
Fig. 3. Aerial orthophoto of the experiment. 1: UFU-199#2#2#1; 2: UFU-7#2#1#1; 3: UFU-117#1#3#1; 4: UFU-86#1#2#1; 5: UFU-75#1#1#1; 6: 199#6#1#1; 7: UFU-206#3#2#1; 8: UFU-199#2#3#1; 9: UFU-189#2#3#1; 10: UFU-184#2#5#1; 11: UFU-184#2#1#1; 12: UFU-107#1#2#1; 13: 86#2#1#1; 14: UFU-75#3#2#1; 15: UFU-184#2#3#1; 16: UFU-199#6#2#1; 17: UFU-7#1#1#1; 18: UFU-75#2#2#1; 19: UFU-75#3#1#1; 20: 190#1#2#1; 21: UFU-75#1#3#1; 22: UFU-189#2#2#1; 23: UFU-189#2#1#1; 24: UFU-199#2#1#1; 25: UFU-199#5#1#1; 26: UFU-206#1#6#1; 27: 206#1#3#1; 28: UFU-117#1#1#1; 29: UFU-184#2#1; 30: UFU-199#1#1#1; 31: Belíssima.
UFUUFUUFUUFU-
et al., 2015; Nick and Borém, 2016). Some reports show that the red coloration is associated with higher levels of anthocyanin, which confer lower photosynthetic rates and results in smaller plants (Sala and Costa, 2016; Zhang et al., 2016). UFU-7#1#1#1, UFU-190#1#2#1 and UFU-199#5#1#1 showed greater tolerance to bolting than the commercial cultivar Belíssima (13.6, 18.6 and 21.3 days, respectively) (Table 1). Higher than ideal temperatures for lettuce cultivation may cause early bolting and latex
indicators of green leaf lettuce cultivars outperform those of red cultivars (Blat et al., 2011). All inbred lines in the current study and especially UFU-199#6#1#1 produced significantly larger plant diameters than did cv. Belíssima (5% probability) (Table 1). Nevertheless, these plant diameters were smaller than those found in other studies for green leaf cultivars (Silva et al., 2000; Diamante et al., 2013; Santi et al., 2013). This inferior performance could be explained by the fact that green leaf cultivars have superior agronomic performance (Becker 157
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obtained in this work were observed by Dutta Gupta et al. (2013); Zaman-Allah et al. (2015) and Kyratzis et al. (2017). The aerial image allows to photograph the whole plot, and the information of all the plants collected together. Among other applications, UAV can be used to determine planting density (Gnädinger and Schmidhalter, 2017) and to obtain information regarding the development of the leaf area over time, making it possible to know the use of water and the behavior of the crop under water deficit during its phenological stages (Potgieter et al., 2017).
Table 2 Relative contribution of seven agronomic characters to genetic divergence among 31 red leaf lettuce inbred lines, according to the criteria of Singh (1981). Characters
1
FW SD LN PD LT SPAD/carotenoid DBS
S.j
S.j (%)
243.55 352.71 2644.53 1978.69 208.81 13697.70 3585.96
1.07 1.55 11.64 8.71 0.92 60.31 15.79
3.2. Confirmation of genetic diversity within the germplasm bank
1
FW: fresh weight (g), SD: stem diameter (cm), LN: leaf number, PD: plant diameter (cm), LT: leaf temperature (ºC), SPAD/carotenoid: SPAD/carotenoid index, (DBS) days to bolting after sowing (days).
In addition to comparing agronomic performance (Table 1), the genotypes were also separated into distinct groups using dissimilarity measures, which may prove useful for lettuce breeders (Araujo et al., 2016). The genetic dissimilarity measures (based on the Mahalanobis generalized distance, D2ii´ ) among the 31 treatments ranged from 1.2 (UFU-75#3#2#1) to 564.4 (UFU-199#1#1#1), indicating strong genetic diversity. The groups formed in the UPGMA dendrogram (Fig. 1) had a cophenetic correlation coefficient of 0.89 (t-test, p < 0.01). Thus, the dendrogram satisfactorily reflected the matrix data and subsequent groupings. The groups were separated using a 20% cutoff line that was established at points of abrupt change in the branches of the dendrogram (Cruz et al., 2012). Group I consisted of 4 lines, group II - 8 lines, group III - 8 lines, group IV - 1 line, group V - 1 line, group VI - 7 lines while group VII consisted of the commercial cultivar Belíssima and 1 line. Thus, the germplasm evaluated in the present study is genetically diverse and differs substantially from the commercial cultivar Belíssima (Fig. 1). Before determining the effectiveness of image phenotyping, it is first necessary to confirm genetic variability within the germplasm. Thus, a second methodology was used to guarantee the existence of genetic variability among the 31 lettuce lines (Fig. 2). The graphical Torcher optimization method can be used to show miniscule genetic dissimilarities between two lines. Values close to zero indicate greater similarity (yellow) while values close to 1 indicate greater genetic dissimilarity (black). The results in Fig. 2 show that there is substantial genetic variability among the lines, meaning that image phenotyping can be used with greater assurance. The SPAD/carotenoid index (Cassetari et al., 2015) contributed more to divergence among the genotypes (60.31% of total variability) than any other variable (Table 2).
production that makes leaves bitter, tough and initiates the reproductive cycle (Da Luz et al., 2009; Aquino et al., 2014), which diminishes product quality and revenue. UFU-199#6#2#1, UFU-75#2#2#1, UFU-199#2#1#1, UFU206#1#3#1 and UFU-199#1#1#1 showed higher SPAD/carotenoid index (138.1%, 142.6%, 143.8%, 135.2% and 154.0%, respectively), than the commercial Belíssima cultivar (Table 1). Several studies have shown the efficiency of SPAD index as an alternative, instantaneous measure of chlorophyll levels in plant leaves (Klooster et al., 2012). Chlorophyll content is highly correlated with carotenoid concentration in lettuce (Cassetari et al., 2015) and therefore, SPAD can be used to indirectly evaluate the carotenoid content in these plants (Cassetari et al., 2015). Even though these measurements can be made quickly, the time needed to evaluate an entire experiment is still high, which complicates evaluations and increases research costs. Measurement of carotenoid content by SPAD lasted about 32 h. Flight duration, image processing and measurement lasted five hours. This time was six times lower when compared to SPAD technique. In addition, the measurement using SPAD/carotenoid was performed in four plants, while the UAV makes it possible to quantify the carotenoid content in the sixteen plants in the experimental plot, being therefore four times more efficient. The value of determining carotenoid levels using a traditional methodology is $ 1000 per sample (Kimura and Rodriguez-Amaya, 2002). In an attempt to reduce cost per sample and seek greater agility, Cassetari et al. (2015) observed a high correlation between the amount of carotenoids and the SPAD index. However, this method is time consuming and costly due to the labor demand required for its execution. In addition, solar irradiance influences the SPAD index result (Hoel and Solhaug, 1998). In this way, the interval between the first evaluation and the last one can interfere in the final result of the SPAD index. In this context, image phenotyping can be an excellent alternative for determining the amount of carotenoids, aiming at obtaining results quickly and at extremely low cost. Aerial sensing for phenotyping promotes fast and objective results, being a non-invasive and low cost process. Results similar to those
3.3. Validation of image phenotyping for the identification of genetic diversity using SPAD/carotenoid index To validate the applicability of image phenotyping, it was first necessary to show genetic variability within the results. Table 1 shows variability among the characteristics. The SPAD/carotenoid index is
Fig. 4. Histogram of the SPAD/carotenoid index for the carotenoid-rich UFU-199#1#1#1 inbred line (left figure). Histogram of the SPAD/carotenoid index of cv. Belíssima with the lowest carotenoid levels (right figure). 158
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Fig. 5. Heat map with interpolated SPAD/carotenoid index values for each lettuce line/cultivar. 1: UFU-199#2#2#1; 2: UFU-7#2#1#1; 3: UFU-117#1#3#1; 4: UFU-86#1#2#1; 5: UFU-75#1#1#1; 6: UFU-199#6#1#1; 7: UFU-206#3#2#1; 8: UFU-199#2#3#1; 9: UFU-189#2#3#1; 10: UFU-184#2#5#1; 11: UFU184#2#1#1; 12: UFU-107#1#2#1; 13: UFU-86#2#1#1; 14: UFU-75#3#2#1; 15: UFU-184#2#3#1; 16: UFU-199#6#2#1; 17: UFU-7#1#1#1; 18: UFU75#2#2#1; 19: UFU-75#3#1#1; 20: UFU-190#1#2#1; 21: UFU-75#1#3#1; 22: UFU-189#2#2#1; 23: UFU-189#2#1#1; 24: UFU-199#2#1#1; 25: UFU199#5#1#1; 26: UFU-206#1#6#1; 27: UFU-206#1#3#1; 28: UFU-117#1#1#1; 29: UFU-184#2#1; 30: UFU-199#1#1#1; 31: Belíssima.
4. Conclusion
especially notable, ranging from a low of 17.6 for cv. Belíssima to a high of 44.7 for UFU-199#1#1#1. Genetic variability within the germplasm bank is confirmed by the UPGMA dendogram (Fig. 1) and the graphical Tocher optimization method (Fig. 2). These were used to determine which individual response variable contributed most. The SPAD/carotenoid index (Table 2) contributed most to genetic diversity among the lettuce inbred lines and was therefore correlated with the mean intensity of the green channel (G). Thus, a linear regression model was performed between the mean green channel (G) values and the SPAD/ carotenoid index. Fig. 3 shows an orthophoto and identifies the various treatments. The following linear regression equation G = -51.88 x SPAD + 251.829 was fit to the mean response variable of the green channel (G) (r = -0.8732, coefficient of determination = 76.25%). The model shows that as the SPAD/carotenoid index increases, the spectral response in the green channel decreases (Fig. 4). The SPAD/carotenoid index was 44.7 and the mean intensity of the spectral response on the G-channel was 47.1 for UFU-199#1#1#1. The same variables for cv. Belíssima were 17.6 and 198.2 respectively. It shows that the higher the SPAD/carotenoid index in the plant, the smaller the response in the green channel (Fig. 4). A heat map was generated that was used to visually interpret correlation intensities (Fig. 5). Warm colors (reds) in the heat map are associated with high SPAD/carotenoid index values, while cool colors (blues) are associated with low values. Geotechnologies are increasingly used in various studies on vegetables (Johann et al., 2012; Risso et al., 2012; Santi et al., 2012; De C Victoria et al., 2012; Vicente et al., 2012; Picoli et al., 2013; ZamanAllah et al., 2015; Vergara-Díaz et al., 2016), except for lettuce. The results show that high performance image phenotyping is an efficient method for selecting lettuce genotypes based on carotenoid/SPAD levels and may be a useful alternative for breeding programs or public efforts to monitor biofortification levels in crops using UAV.
The germplasm bank analyzed in this study possessed substantial genetic variability. High performance image phenotyping was highly correlated with a traditional methodology and may provide an alternative for identifying different genetic backgrounds within a germplasm bank. In this study, an RGB camera was used, but in future works, we intend to use a camera with an infrared sensor, with a higher spectral resolution that will allow investigating phenotypic characteristics that do not appear in the visible spectrum (RGB). Declarations of interest None. Acknowledgements The authors thank CNPq, CAPES, FAPEMIG, PROPP and UFU for financial and administrative support. References ABCSEM, 2016. Associação Brasileira de Comércio de Sementes e Mudas. Folhosas: Seminário Nacional. Disponível em. Acesso em: 31 Dez. 2018. http://www.abcsem. com.br/upload/arquivos/O_mercado_de_folhosas__Numeros_e_Tendencias_-_Steven. pdf. de Aquino, C.R., Seabra Junior, S., Camili, E.C., Diamante, M.S., Pinto, E.S.C., 2014. Produção e tolerância ao pendoamento de alface-romana em diferentes ambientes. Rev. Ceres. 61 (4), 558–566. https://doi.org/10.1590/0034-737X201461040016. Araujo, J.C., Telhado, S.F.P., Sakai, R.H., Ledo, C.A.S., Melo, P.C.T., 2016. Univariate and multivariate procedures for agronomic evaluation of organically grown tomato cultivars. Hortic. Bras. 34 (3), 374–380. https://doi.org/10.1590/S010205362016003011. Becker, C., Urlić, B., Špika, M.J., Kläring, H.-P., Krumbein, A., Baldermann, S., Ban, S.G., Perica, S., Schwarz, D., 2015. Nitrogen limited red and green leaf lettuce accumulate
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Image phenotyping of inbred red lettuce lines with genetic diversity regarding carotenoid levels
T
Gabriel Mascarenhas Maciel, Rodrigo Bezerra de Araújo Gallis, Ricardo Luís Barbosa, ⁎ Lucas Medeiros Pereira, Ana Carolina Silva Siquieroli, Joicy Vitória Miranda Peixoto Universidade Federal de Uberlândia, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Geotechnology Biofortification Germplasm bank Genetic dissimilarity Unmanned aerial vehicle (UAV)
Developing biofortified foods is a goal of genetic breeding programs. However, analysis costs and the time required for leaf sampling in the field are hindrances to this process. The objectives of this study were to evaluate the genetic diversity in red leaf lettuce germplasm and to evaluate the use of image phenotyping for the identification of carotenoid-rich genotypes. The experiment was carried out in 2018 at the Vegetable Experiment Station of the Federal University of Uberlândia-Monte Carmelo campus. Thirty inbred lines of red leaf lettuce were evaluated. All inbred lines resulted from the hybridization of the Belíssima and Uberlândia 10000 cultivars and six successive selfings, carried out from 2013 to 2017. The genealogical method or pedigree is a working procedure used by plant breeders working with plant species whose reproductive system is autogamous and presenting cleistogamy. This method was used to obtain the treatments of the experiment. The cultivar Belíssima (red lettuce) was used as a control, totaling 31 treatments. A conventional method and an aerial image phenotyping using Phantom 4 unmanned aerial vehicle (UAV) were used to evaluate six different agronomic characteristics and carotenoid levels of each treatment. The results showed substantial genetic diversity within the germplasm bank. Furthermore, high performance image phenotyping was highly correlated with the traditional methodology (r = -0.8732, coefficient of determination = 76.25%) and can therefore be considered an alternative for identifying different genetic backgrounds within a germplasm bank. Unmanned aerial vehicle (UAV) might also be used to monitor biofortification levels in crops.
1. Introduction Lettuce (Lactuca sativa L.) comes in various shapes and colors that are represented by four different categories (crispy, smooth, romaine and crisphead) (Sala and da Costa, 2012). Current lettuce genetic diversity allows farmers to select from various options. In Brazil, leaf lettuce is more popular than crisphead, smooth and romaine lettuce (Sala et al., 2008; Sala and da Costa, 2012). Among the various leaf lettuces grown in Brazil, 2360 ha are dedicated to red lettuce (ABCSEM, 2016). The cultivation of red leaf lettuce has allowed growers to add value and reach specific market niches. In addition to being visually attractive, the consumption of red lettuce can help prevent several diseases related to oxidative stress (Maiani et al., 2009; Rocha and Reed, 2014) due to the presence of carotenoids, which are precursors to vitamin A (Sousa et al., 2007; Silva and Mura, 2010; Cassetari et al., 2015). Despite these qualitative and commercial advantages, there are currently no red leaf cultivars that are rich in carotenoids. Sousa et al. (2007) ⁎
identified a green, smooth-leaved cultivar that is rich in carotenoids (Uberlândia 10,000). However, few studies have sought to obtain carotenoid-rich lettuce cultivars. One probable reason may be related to the high cost of analyzing leaf constituents and the time needed to carry out laboratory analyses. Some studies have demonstrated more economical and efficient methodologies that can benefit breeding programs. Cassetari et al. (2015) suggested that the SPAD index could be used to indirectly quantify carotenoids from leaf chlorophyll content. However, other methodologies exist that could revolutionize the phenotyping of plants with different carotenoid levels. One new alternative is image phenotyping, which has already been used to select various types of vegetables (De Sousa et al., 2015), but not lettuce. Image phenotyping can be used to determine qualitative and/or quantitative values and correlate these with genotype performance within a given environment (Dhondt et al., 2013). Plant phenotyping offers non-destructive optical analyses of plant characteristics, mainly via images (Walter et al., 2015). Current techniques employ a
Corresponding author. E-mail address: [email protected] (J. Vitória Miranda Peixoto).
https://doi.org/10.1016/j.jag.2019.05.016 Received 3 January 2019; Received in revised form 10 May 2019; Accepted 13 May 2019 Available online 23 May 2019 0303-2434/ © 2019 Elsevier B.V. All rights reserved.
Int J Appl Earth Obs Geoinformation 81 (2019) 154–160
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centimeters. Leaf temperature was determined by aiming an infrared thermometer (model 4000.4 G L, Everest Interscience, Tucson, AZ, USA) at the center of an upper leaf. The SPAD/carotenoid index (Cassetari et al., 2015) was calculated as the mean of the index from the four central plants of each plot. The reading was made with a chlorophyll meter (Minolta SPAD-502 CFL1030) in triplicate, in both lateral sheets, opposite each other and in the intermediate sheet of each plant. The number of days from sowing to bolting (DBS) was also recorded. The data were submitted to analysis of variance (F test, p ≤ 0.05) and the means compared by the Scott-Knott test (p < 0.05). Then, multivariate analyses of genetic dissimilarity between genotypes were determined using the Mahalanobis generalized distance (D2ii´ ). Genetic divergence was represented by a dendrogram that was obtained using the hierarchical Unweighted Pair-Group Method Using Arithmetic Averages (UPGMA) and graphical Tocher optimization. UPGMA clustering was evaluated using a cophenetic correlation coefficient (CCC) calculated by the Mantel (1967) test. The relative contribution of quantitative traits was calculated according to Singh (1981). All data were analyzed using Genes version 2015.5.0 software (Cruz, 2013). In addition to using a conventional methodology based on the SPAD/carotenoid index (Cassetari et al., 2015), image phenotyping of the same genotypes was performed using aerial images taken with a RGB camera with CMOS sensor and 9 mm of focal lenght mounted on a Phantom 4 unmanned aerial vehicle (UAV). The images were collected with 4864 × 3648 pixels and stored in JPEG format. The flight parameters were: 20-meter height, 80% longitudinal overlap and 75% lateral overlap. The flight was performed automatically using proprietary DroneDeploy software. The resulting images were used to generate an orthophoto with a GSD (Ground Sample Distance) of 1 cm (Pix4d software). The heat map was generated from the georeferencing of the four plants of the useful area by means of an orthophoto. Then, using the average value obtained by SPAD, kriging was performed with the objective of extrapolating the result observed in the useful area to the total plot area. Histograms were generated for the two most contrasting lines in SPAD/carotenoid index. Each plot was manually delimited in the orthophoto and ImageJ v. 1.52 software was used to isolate the green channel RGB image. Next, mean green intensity values (G) were calculated for each plot and then correlated with SPAD values using SIGMA PLOT software (5% significance). The software QGis v. 3.0 was used in addition to the correlation, to visualize the geo-spatial distribution of the SPAD/carotenoid index with the heat map generated using the IDW (Inverse Distance Weighted) function as a kernel.
multidisciplinary approach that includes spectroscopy and image generation (De Sousa et al., 2015). These images can be easily collected using unmanned aerial vehicle (UAV), which are also known as drones. These aircraft can carry various types of cameras with sensors that capture images ranging from visible electromagnetic to infrared that can be used in various vegetation analyses (De Sousa et al., 2015; Yang et al., 2017). The resulting combination of images with high spatial and temporal resolution is one of the main attractions for using this technology in lettuce phenotyping. Current studies have attempted to correlate vegetation reflectance with the characteristics of several plant species (Antunes et al. 2012; Johann et al., 2012; Risso et al., 2012; Santi et al., 2012; De C Victoria et al., 2012; Picoli et al., 2013; Vicente et al., 2012; Makanza et al., 2018), but not vegetables. The use of imaging techniques to aid in the indirect selection of carotenoid-rich lettuce genotypes is not yet a reality. Therefore, the objective of this study is to determine genetic diversity in germplasm of red leaf lettuce and to validate the use of image phenotyping in identifying carotenoid-rich genotypes. 2. Material and methods The experiment was carried out in 2018 at the Vegetable Experiment Station of the Federal University of Uberlândia - Monte Carmelo campus (18°42′43.19″ S, 47°29′55.8″ W, 873 m altitude), which is part of the UFU’s Breeding Program for Biofortified and Tropicalized Lettuce. The study evaluated 30 inbred lines of red leaf lettuce that were hybridized from the Belíssima cultivar and the carotenoid-rich Uberlândia 10,000 cultivar (Sousa et al., 2007) and six successive selfings carried out between 2013 and 2017. The genealogical method or pedigree is a working procedure used by plant breeders working with plant species whose reproductive system is autogamous and presenting cleistogamy. This method was used to obtain the treatments of the experiment. The cultivar Belíssima (red lettuce), widely cultivated in Brazil, was used as a control. This cultivar together with 30 more red leaf lettuce genotypes totaled the 31 treatments evaluated in the experiment. The pedigree method is based on individual plant selection in the segregating population. Evaluation was performed on each individual progeny. The superiority of the selected individuals was analyzed by the progeny test. The selection with progeny testing and knowledge of the genealogy of the chosen individuals allow the maximization of selection efficiency. The screening was carried out based on the genotypes of individuals as per Borém et al. (2017) method. Sowing took place on March 28, 2018. The seedlings were produced in 200-cell expanded polystyrene trays filled with a commerciallyavailable coconut fiber substrate. After sowing, the trays were maintained in a hoop-style greenhouse (area: 5 m x 6 m, height: 3.5 m), covered with UV resistant polyethylene film (150 microns) and antiaphid side screens. At 29 days after sowing, the seedlings were transplanted to field beds that had been prepared with a rototiller-hiller (1.3 m wide). Before setting up the experiment, soil samples were taken from a depth of 0 cm–20 cm and analyzed at the Soil Fertility Laboratory of the Federal University of Uberlândia. The physical/chemical analysis showed the following: clayey texture (> 50%), pH in CaCl2 = 4.9, SOM = 3.9 dag kg−1, P(rem) =79.1 mg dm-3, K = 0.29 cmolc dm-3, Ca = 3.3 cmolc dm-3, Mg = 1.3 cmolc dm-3, H + Al = 4.9 cmolc dm-3, SB = 4.90 cmolc dm-3, CEC = 9.80 cmolc dm-3, BS% = 50. Crop treatments were carried out as recommended for lettuce (Filgueira, 2013). Each experimental plot (Fig. 3) consisted of 16 plants in four rows (spaced 0.25 m x 0.25 m), from which the eight centermost plants were evaluated. The plants were harvested 35 days after transplant and taken to a laboratory where total fresh weight was determined by weighing all outer leaves. Stem diameter was measured with calipers. Numbers of commercially viable leaves were determined by counting leaves over 5 cm in length. Plant diameter was measured and expressed in
3. Results and discussion 3.1. Agronomic evaluation of germplasm During the evaluation period (September – November) the maximum temperatures varied from 36.5 °C to 19.5 °C (mean =30 °C) while the minimum temperatures varied from 22.7 °C to 11.8 °C (mean =17.6 °C), (SISMET, 2018). These temperatures were above the optimal range for lettuce cultivation, which is 4 °C to 27 °C (Do Santos et al., 2009). Significant differences (F test, p < 0.05) were found among the lettuce inbred lines for all variables except stem diameter and leaf temperature (Table 1). The fresh weight values of UFU-199#6#1#1, UFU-75#1#3#1, UFU-199#2#3#1 and UFU-184#2#1#1 differed most significantly from the other inbred lines, including the control, cv Belíssima (Table 1). The fresh weight of the most exceptional lines in the present study were greater than those found for the red leaf cultivars Pira Roxa and Belíssima (Blat et al., 2011), and similar to values found in other studies (Diamante et al., 2013; Suinaga et al., 2013; Aquino et al., 2014; 155
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Table 1 Fresh weight (FW), stem diameter (SD), leaf number (LN), plant diameter (PD), leaf temperature (LT), days to bolting after sowing (DBS) and the SPAD/carotenoid index of various inbred lines of leaf lettuce (SPAD). Treatments
FW (g)
UFU-199#2#2#1 UFU-7#2#1#1 UFU-117#1#3#1 UFU-86#1#2#1 UFU-75#1#1#1 UFU-199#6#1#1 UFU-206#3#2#1 UFU-199#2#3#1 UFU-189#2#3#1 UFU-184#2#5#1 UFU-184#2#1#1 UFU-107#1#2#1 UFU-86#2#1#1 UFU-75#3#2#1 UFU-184#2#3#1 UFU-199#6#2#1 UFU-7#1#1#1 UFU-75#2#2#1 UFU-75#3#1#1 UFU-190#1#2#1 UFU-75#1#3#1 UFU-189#2#2#1 UFU-189#2#1#1 UFU-199#2#1#1 UFU-199#5#1#1 UFU-206#1#6#1 UFU-206#1#3#1 UFU-117#1#1#1 UFU-184#2#1 UFU-199#1#1#1 cv. Belíssima Overall average CV(%)
88.58 b 38.33 e 44.42 e 56.25 d 63.67 c 120.33 a 100.75 b 116.75 a 30.25 f 42.83 e 116.67 a 49.58 d 40.83 e 31.33 f 55.58 d 66.00 c 44.08 e 70.58 c 63.00 c 27.67 f 117.58 a 38.25 e 41.08 e 89.08 b 52.42 d 49.08 d 53.50 d 24.67 f 69.75 c 74.92 c 23.25 f 61.33 14.42
SD (cm)
1.7 a 1.5 a 1.3 a 1.7 a 1.1 a 1.9 a 1.6 a 2.0 a 1.4 a 1.7 a 1.7 a 1.6 a 1.8 a 1.2 a 1.9 a 1.8 a 1.8 a 1.5 a 1.7 a 1.3 a 1.7 a 1.2 a 1.6 a 1.1 a 2.3 a 1.9 a 1.1 a 1.3 a 1.9 a 1.2 a 1.3 a 1.6 40.29
LN
16.7 a 15.9 b 17.8 a 14.8 b 17.7 a 18.1 a 12.0 b 14.8 b 16.1 b 19.9 a 19.9 a 17.8 a 15.5 b 10.8 b 20.5 a 15.2 b 20.1 a 13.3 b 12.4 b 14.3 b 22.6 a 13.1 b 17.7 a 15.3 b 18.7 a 15.8 b 14.5 b 12.2 b 19.8 a 15.1 b 8.3 b 16.0 16.78
PD (cm)
LT (ºC)
DBS (days)
Index SPAD/Carotenoid
RS1 (%)
23.3 a 20.8 a 21.5 a 23.2 a 23.1 a 27.2 a 18.0 b 21.3 a 20.6 a 21.0 a 25.6 a 19.3 b 20.4 a 18.5 b 26.0 a 22.2 a 22.3 a 21.2 a 21.3 a 15.3 b 24.5 a 15.0 b 21.0 a 21.9 a 18.7 b 22.4 a 19.3 b 16.0 b 24.6 a 24.4 a 10.9 b 21.0 17.13
25.0 a 24.2 a 24.5 a 23.1 a 24.6 a 24.3 a 24.4 a 23.6 a 25.1 a 24.4 a 24.0 a 25.0 a 24.5 a 25.1 a 24.6 a 23.9 a 23.7 a 23.9 a 23.5 a 24.9 a 23.3 a 25.1 a 26.0 a 24.1 a 24.5 a 24.7 a 26.1 a 25.2 a 23.7 a 23.9 a 24.1 a 24.4 6.37
81.0 c 93.3 b 84.0 c 85.3 c 84.0 c 83.3 c 77.0 c 83.3 c 83.7 c 98.7 b 83.0 c 87.0 c 90.7 c 82.3 c 92.7 b 86.0 c 100.3 a 85.7 c 80.7 c 105.3 a 96.7 b 88.0 c 91.7 b 81.0 c 108.0 a 89.0 c 81.0 c 85.3 c 94.3 b 86.7 c 86.7 c 88.2 4.87
38.9 b 29.2 e 23.5 f 24.4 f 34.7 c 38.5 b 29.6 e 28.3 e 28.1 e 37.7 b 27.0 e 33.6 c 30.4 d 31.4 d 32.1 d 41.9 a 31.6 d 42.7 a 31.4 d 26.6 e 34.3 c 29.2 e 34.6 c 42.9 a 37.9 b 28.9 e 41.4 a 18.6 g 35.5 c 44.7 a 17.6 g 32.5 5.58
121.0 65.9 33.5 38.6 97.2 118.8 68.2 60.8 59.7 114.2 53.4 90.9 72.7 78.4 82.4 138.1 79.5 142.6 78.4 51.1 94.9 65.9 96.6 143.8 115.3 64.2 135.2 5.7 101.7 154.0 0.0
Means followed by distinct letters within a column differ by the Scott-Knott test (0.05 significance). 1(RS) Relative superiority of the SPAD/carotenoid index of lettuce inbred lines compared to the control, cv. Belíssima.
Fig. 1. Dendrogram of genetic divergence among 30 lettuce inbred lines and a commercial control (cv. Belíssima) - obtained using the hierarchical Unweighted PairGroup Method (UPGMA) and dissimilarity measures.
numbers that were significantly greater than cv. Belíssima (Scott-knott, p ≤ 0.05) (Table 1). Similar leaf numbers have been found by other authors (Do Santos et al., 2009; Diamante et al., 2013; Blat et al., 2011; Aquino et al., 2014). To date, there has been limited research on the agronomic performance of red leaf lettuce cultivars. However, in general, the agronomic
Ziech et al., 2014; Brzezinski et al., 2017), suggesting that these red leaf lettuces meet the standards of commercial cultivars. UFU-199#2#2#1, UFU-117#1#3#1, UFU-75#1#1#1, UFU199#6#1#1, UFU-184#2#5#1, UFU-184#2#1#1, UFU-107#1#2#1, UFU-184#2#3#1, UFU-7#1#1#1, UFU-75#1#3#1, UFU189#2#1#1, UFU-199#5#1#1 and UFU-184#2#1 produced leaf 156
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Fig. 2. Clustering by graphical Tocher optimization, based on seven agronomic characteristics of 31 red leaf lettuce inbred lines. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
Fig. 3. Aerial orthophoto of the experiment. 1: UFU-199#2#2#1; 2: UFU-7#2#1#1; 3: UFU-117#1#3#1; 4: UFU-86#1#2#1; 5: UFU-75#1#1#1; 6: 199#6#1#1; 7: UFU-206#3#2#1; 8: UFU-199#2#3#1; 9: UFU-189#2#3#1; 10: UFU-184#2#5#1; 11: UFU-184#2#1#1; 12: UFU-107#1#2#1; 13: 86#2#1#1; 14: UFU-75#3#2#1; 15: UFU-184#2#3#1; 16: UFU-199#6#2#1; 17: UFU-7#1#1#1; 18: UFU-75#2#2#1; 19: UFU-75#3#1#1; 20: 190#1#2#1; 21: UFU-75#1#3#1; 22: UFU-189#2#2#1; 23: UFU-189#2#1#1; 24: UFU-199#2#1#1; 25: UFU-199#5#1#1; 26: UFU-206#1#6#1; 27: 206#1#3#1; 28: UFU-117#1#1#1; 29: UFU-184#2#1; 30: UFU-199#1#1#1; 31: Belíssima.
UFUUFUUFUUFU-
et al., 2015; Nick and Borém, 2016). Some reports show that the red coloration is associated with higher levels of anthocyanin, which confer lower photosynthetic rates and results in smaller plants (Sala and Costa, 2016; Zhang et al., 2016). UFU-7#1#1#1, UFU-190#1#2#1 and UFU-199#5#1#1 showed greater tolerance to bolting than the commercial cultivar Belíssima (13.6, 18.6 and 21.3 days, respectively) (Table 1). Higher than ideal temperatures for lettuce cultivation may cause early bolting and latex
indicators of green leaf lettuce cultivars outperform those of red cultivars (Blat et al., 2011). All inbred lines in the current study and especially UFU-199#6#1#1 produced significantly larger plant diameters than did cv. Belíssima (5% probability) (Table 1). Nevertheless, these plant diameters were smaller than those found in other studies for green leaf cultivars (Silva et al., 2000; Diamante et al., 2013; Santi et al., 2013). This inferior performance could be explained by the fact that green leaf cultivars have superior agronomic performance (Becker 157
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obtained in this work were observed by Dutta Gupta et al. (2013); Zaman-Allah et al. (2015) and Kyratzis et al. (2017). The aerial image allows to photograph the whole plot, and the information of all the plants collected together. Among other applications, UAV can be used to determine planting density (Gnädinger and Schmidhalter, 2017) and to obtain information regarding the development of the leaf area over time, making it possible to know the use of water and the behavior of the crop under water deficit during its phenological stages (Potgieter et al., 2017).
Table 2 Relative contribution of seven agronomic characters to genetic divergence among 31 red leaf lettuce inbred lines, according to the criteria of Singh (1981). Characters
1
FW SD LN PD LT SPAD/carotenoid DBS
S.j
S.j (%)
243.55 352.71 2644.53 1978.69 208.81 13697.70 3585.96
1.07 1.55 11.64 8.71 0.92 60.31 15.79
3.2. Confirmation of genetic diversity within the germplasm bank
1
FW: fresh weight (g), SD: stem diameter (cm), LN: leaf number, PD: plant diameter (cm), LT: leaf temperature (ºC), SPAD/carotenoid: SPAD/carotenoid index, (DBS) days to bolting after sowing (days).
In addition to comparing agronomic performance (Table 1), the genotypes were also separated into distinct groups using dissimilarity measures, which may prove useful for lettuce breeders (Araujo et al., 2016). The genetic dissimilarity measures (based on the Mahalanobis generalized distance, D2ii´ ) among the 31 treatments ranged from 1.2 (UFU-75#3#2#1) to 564.4 (UFU-199#1#1#1), indicating strong genetic diversity. The groups formed in the UPGMA dendrogram (Fig. 1) had a cophenetic correlation coefficient of 0.89 (t-test, p < 0.01). Thus, the dendrogram satisfactorily reflected the matrix data and subsequent groupings. The groups were separated using a 20% cutoff line that was established at points of abrupt change in the branches of the dendrogram (Cruz et al., 2012). Group I consisted of 4 lines, group II - 8 lines, group III - 8 lines, group IV - 1 line, group V - 1 line, group VI - 7 lines while group VII consisted of the commercial cultivar Belíssima and 1 line. Thus, the germplasm evaluated in the present study is genetically diverse and differs substantially from the commercial cultivar Belíssima (Fig. 1). Before determining the effectiveness of image phenotyping, it is first necessary to confirm genetic variability within the germplasm. Thus, a second methodology was used to guarantee the existence of genetic variability among the 31 lettuce lines (Fig. 2). The graphical Torcher optimization method can be used to show miniscule genetic dissimilarities between two lines. Values close to zero indicate greater similarity (yellow) while values close to 1 indicate greater genetic dissimilarity (black). The results in Fig. 2 show that there is substantial genetic variability among the lines, meaning that image phenotyping can be used with greater assurance. The SPAD/carotenoid index (Cassetari et al., 2015) contributed more to divergence among the genotypes (60.31% of total variability) than any other variable (Table 2).
production that makes leaves bitter, tough and initiates the reproductive cycle (Da Luz et al., 2009; Aquino et al., 2014), which diminishes product quality and revenue. UFU-199#6#2#1, UFU-75#2#2#1, UFU-199#2#1#1, UFU206#1#3#1 and UFU-199#1#1#1 showed higher SPAD/carotenoid index (138.1%, 142.6%, 143.8%, 135.2% and 154.0%, respectively), than the commercial Belíssima cultivar (Table 1). Several studies have shown the efficiency of SPAD index as an alternative, instantaneous measure of chlorophyll levels in plant leaves (Klooster et al., 2012). Chlorophyll content is highly correlated with carotenoid concentration in lettuce (Cassetari et al., 2015) and therefore, SPAD can be used to indirectly evaluate the carotenoid content in these plants (Cassetari et al., 2015). Even though these measurements can be made quickly, the time needed to evaluate an entire experiment is still high, which complicates evaluations and increases research costs. Measurement of carotenoid content by SPAD lasted about 32 h. Flight duration, image processing and measurement lasted five hours. This time was six times lower when compared to SPAD technique. In addition, the measurement using SPAD/carotenoid was performed in four plants, while the UAV makes it possible to quantify the carotenoid content in the sixteen plants in the experimental plot, being therefore four times more efficient. The value of determining carotenoid levels using a traditional methodology is $ 1000 per sample (Kimura and Rodriguez-Amaya, 2002). In an attempt to reduce cost per sample and seek greater agility, Cassetari et al. (2015) observed a high correlation between the amount of carotenoids and the SPAD index. However, this method is time consuming and costly due to the labor demand required for its execution. In addition, solar irradiance influences the SPAD index result (Hoel and Solhaug, 1998). In this way, the interval between the first evaluation and the last one can interfere in the final result of the SPAD index. In this context, image phenotyping can be an excellent alternative for determining the amount of carotenoids, aiming at obtaining results quickly and at extremely low cost. Aerial sensing for phenotyping promotes fast and objective results, being a non-invasive and low cost process. Results similar to those
3.3. Validation of image phenotyping for the identification of genetic diversity using SPAD/carotenoid index To validate the applicability of image phenotyping, it was first necessary to show genetic variability within the results. Table 1 shows variability among the characteristics. The SPAD/carotenoid index is
Fig. 4. Histogram of the SPAD/carotenoid index for the carotenoid-rich UFU-199#1#1#1 inbred line (left figure). Histogram of the SPAD/carotenoid index of cv. Belíssima with the lowest carotenoid levels (right figure). 158
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Fig. 5. Heat map with interpolated SPAD/carotenoid index values for each lettuce line/cultivar. 1: UFU-199#2#2#1; 2: UFU-7#2#1#1; 3: UFU-117#1#3#1; 4: UFU-86#1#2#1; 5: UFU-75#1#1#1; 6: UFU-199#6#1#1; 7: UFU-206#3#2#1; 8: UFU-199#2#3#1; 9: UFU-189#2#3#1; 10: UFU-184#2#5#1; 11: UFU184#2#1#1; 12: UFU-107#1#2#1; 13: UFU-86#2#1#1; 14: UFU-75#3#2#1; 15: UFU-184#2#3#1; 16: UFU-199#6#2#1; 17: UFU-7#1#1#1; 18: UFU75#2#2#1; 19: UFU-75#3#1#1; 20: UFU-190#1#2#1; 21: UFU-75#1#3#1; 22: UFU-189#2#2#1; 23: UFU-189#2#1#1; 24: UFU-199#2#1#1; 25: UFU199#5#1#1; 26: UFU-206#1#6#1; 27: UFU-206#1#3#1; 28: UFU-117#1#1#1; 29: UFU-184#2#1; 30: UFU-199#1#1#1; 31: Belíssima.
4. Conclusion
especially notable, ranging from a low of 17.6 for cv. Belíssima to a high of 44.7 for UFU-199#1#1#1. Genetic variability within the germplasm bank is confirmed by the UPGMA dendogram (Fig. 1) and the graphical Tocher optimization method (Fig. 2). These were used to determine which individual response variable contributed most. The SPAD/carotenoid index (Table 2) contributed most to genetic diversity among the lettuce inbred lines and was therefore correlated with the mean intensity of the green channel (G). Thus, a linear regression model was performed between the mean green channel (G) values and the SPAD/ carotenoid index. Fig. 3 shows an orthophoto and identifies the various treatments. The following linear regression equation G = -51.88 x SPAD + 251.829 was fit to the mean response variable of the green channel (G) (r = -0.8732, coefficient of determination = 76.25%). The model shows that as the SPAD/carotenoid index increases, the spectral response in the green channel decreases (Fig. 4). The SPAD/carotenoid index was 44.7 and the mean intensity of the spectral response on the G-channel was 47.1 for UFU-199#1#1#1. The same variables for cv. Belíssima were 17.6 and 198.2 respectively. It shows that the higher the SPAD/carotenoid index in the plant, the smaller the response in the green channel (Fig. 4). A heat map was generated that was used to visually interpret correlation intensities (Fig. 5). Warm colors (reds) in the heat map are associated with high SPAD/carotenoid index values, while cool colors (blues) are associated with low values. Geotechnologies are increasingly used in various studies on vegetables (Johann et al., 2012; Risso et al., 2012; Santi et al., 2012; De C Victoria et al., 2012; Vicente et al., 2012; Picoli et al., 2013; ZamanAllah et al., 2015; Vergara-Díaz et al., 2016), except for lettuce. The results show that high performance image phenotyping is an efficient method for selecting lettuce genotypes based on carotenoid/SPAD levels and may be a useful alternative for breeding programs or public efforts to monitor biofortification levels in crops using UAV.
The germplasm bank analyzed in this study possessed substantial genetic variability. High performance image phenotyping was highly correlated with a traditional methodology and may provide an alternative for identifying different genetic backgrounds within a germplasm bank. In this study, an RGB camera was used, but in future works, we intend to use a camera with an infrared sensor, with a higher spectral resolution that will allow investigating phenotypic characteristics that do not appear in the visible spectrum (RGB). Declarations of interest None. Acknowledgements The authors thank CNPq, CAPES, FAPEMIG, PROPP and UFU for financial and administrative support. References ABCSEM, 2016. Associação Brasileira de Comércio de Sementes e Mudas. Folhosas: Seminário Nacional. Disponível em. Acesso em: 31 Dez. 2018. http://www.abcsem. com.br/upload/arquivos/O_mercado_de_folhosas__Numeros_e_Tendencias_-_Steven. pdf. de Aquino, C.R., Seabra Junior, S., Camili, E.C., Diamante, M.S., Pinto, E.S.C., 2014. Produção e tolerância ao pendoamento de alface-romana em diferentes ambientes. Rev. Ceres. 61 (4), 558–566. https://doi.org/10.1590/0034-737X201461040016. Araujo, J.C., Telhado, S.F.P., Sakai, R.H., Ledo, C.A.S., Melo, P.C.T., 2016. Univariate and multivariate procedures for agronomic evaluation of organically grown tomato cultivars. Hortic. Bras. 34 (3), 374–380. https://doi.org/10.1590/S010205362016003011. Becker, C., Urlić, B., Špika, M.J., Kläring, H.-P., Krumbein, A., Baldermann, S., Ban, S.G., Perica, S., Schwarz, D., 2015. Nitrogen limited red and green leaf lettuce accumulate
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