Application of hyperspectral image to identify the salinity effects on lettuce leaves (original) (raw)
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Hyperspectral Imaging to Evaluate the Effect of IrrigationWater Salinity in Lettuce
Applied Sciences, 2016
Salinity is one of the most important stress factors in crop production, particularly in arid regions. This research focuses on the effect of salinity on the growth of lettuce plants; three solutions with different levels of salinity were considered and compared (S1 = 50, S2 = 100 and S3 = 150 mM NaCl) with a control solution (Ct = 0 mM NaCl). The osmotic potential and water content of the leaves were measured, and hyperspectral images of the surfaces of 40 leaves (10 leaves per treatment) were taken after two weeks of growth. The mean spectra of the leaves (n = 32,000) were pre-processed by means of a Savitzky-Golay algorithm and standard normal variate normalization. Principal component analysis was then performed on a calibration set of 28 mean spectra, yielding an initial model for salinity effect detection. A second model was subsequently proposed based on an index computing an approximation to the second derivative at the red edge region. Both models were applied to all the hyperspectral images to obtain the corresponding artificial images, distinguishing between the 28 that were used to extract the calibration mean spectra and the rest that constituted an external validation. Those virtual images were studied using analysis of variance in order to compare their ability for detecting salinity effects on the leaves. Both models showed significant differences between each salinity level, and the hyperspectral images allowed observations of the distribution of the salinity effects on the leaf surfaces, which were more intense in the areas distant from the veins. However, the index-based model is simpler and easier to apply because it is based solely on the reflectance at three different wavelengths, thus allowing for the implementation of less expensive multispectral devices.
Phenomic and Physiological Analysis of Salinity Effects on Lettuce
Sensors, 2019
Salinity is a rising concern in many lettuce-growing regions. Lettuce (Lactuca sativa L.) is sensitive to salinity, which reduces plant biomass, and causes leaf burn and early senescence. We sought to identify physiological traits important in salt tolerance that allows lettuce adaptation to high salinity while maintaining its productivity. Based on previous salinity tolerance studies, one sensitive and one tolerant genotype each was selected from crisphead, butterhead, and romaine, as well as leaf types of cultivated lettuce and its wild relative, L. serriola L. Physiological parameters were measured four weeks after transplanting two-day old seedlings into 350 mL volume pots filled with sand, hydrated with Hoagland nutrient solution and grown in a growth chamber. Salinity treatment consisted of gradually increasing concentrations of NaCl and CaCl2 from 0 mM/0 mM at the time of transplanting, to 30 mM/15 mM at the beginning of week three, and maintaining it until harvest. Across th...
Physiological Diversity of Lettuce Cultivars Exposed to Salinity Stress
2010
Increased soil salinity is a major environmental stress factor for many plants, and limits crop yields. Lettuce is a frequently grown, moderately salt-sensitive crop plant, with many different cultivars. Their physiological diversity when exposed to salt stress enables breeders to efficiently select those cutivars more suitable to grow under conditions of salt stress associated with drought and global warming. The aim of this work is to study the physiological diversity of frequently cultivated lettuce varieties subjected to salinity stress, enabling an efficient selection of more tolerant cultivars. Different functional parameters of induced chlorophyll fluorescence, molar ratios between the main photosynthetic light-harvesting pigments, dry biomass accumulation and seed germination dynamics are evaluated in the context of physiological diversity of lettuce cultivars. Potential and effective light use efficiency is generally decreased only by severe salt stress (exposure to 150 mM ...
A B S T R A C T Realistic simulations of saline field conditions and effective monitoring of phenotyping parameters in an expeditious , non-destructive manner are imperative to successful breeding of genotypes for salinity stress tolerance. This study aimed to spectrally assess the growth, water relations and ion contents of wheat under simulated saline field conditions using the subsurface water retention technique (SWRT) and three salinity water levels (control, 6, and 12 dS m −1). Phenotypic parameters and hyperspectral signatures of the canopy within the 350–2500 nm range were measured at the flowering stage. Multivariate analysis, including correlation, partial least squares regression, simultaneous b-coefficient and variable importance for projection (VIP), and stepwise multiple linear regression were used in the same order, to extract sensitive wavebands and effective singular wavelengths. Binary effective wavelengths as normalized spectral indices (NDSIs) were constructed and related to phenotypic parameters for pooled data and for each salinity level and cultivar. The results confirmed that the shoot dry weight (SDW), water relations and ion contents parameters were effective as screening criteria for evaluating the salt tolerance of wheat cultivars under simulated saline field conditions. It was possible to assess the phenotypic parameters by using hyperspectral canopy signatures over a broad spectrum range. All parameters exhibited stronger relationships with the wavelengths extracted in the visible-infrared (VIS) and red edge regions than those extracted in the near-infrared (NIR) and shortwave-infrared (SWIR) regions. Six wavelengths within the VIS region, five within the red edge and SWIR-1 regions, eleven within the NIR region, and nine within the SWIR-2 region were extracted as effective bands. The NDSIs based on VIS/VIS, red edge/red edge, red edge/VIS, NIR/VIS, NIR/red edge, and NIR/NIR were more appropriate for assessing the phenotypic parameters than indices based on SWIR/SWIR and SWIR/NIR, except for the SDW, K + and Ca 2+ contents, which showed strong correlations with the latter NDSIs. The close relationship between SDW and the water relations and ion contents parameters on one side and the high predictive power of the NDSIs based on the VIS, red edge, and NIR wavelengths in the assessment of phenotypic parameters on the other side indicates that the hyperspectral reflectance data and band selection techniques could be used for the indirect assessment of water relations and ion content of wheat under saline field conditions.
2019
the timely estimation of growth and photosynthetic-related traits in an easy and nondestructive manner using hyperspectral data will become imperative for addressing the challenges of environmental stresses inherent to the agricultural sector in arid conditions. However, the handling and analysis of these data by exploiting the full spectrum remains the determining factor for refining the estimation of crop variables. The main objective of this study was to estimate growth and traits underpinning photosynthetic efficiency of two wheat cultivars grown under simulated saline field conditions and exposed to three salinity levels using hyperspectral reflectance information from 350-2500 nm obtained at two years. Partial least squares regression (PLSR) based on the full spectrum was applied to develop predictive models for estimating the measured parameters in different conditions (salinity levels, cultivars, and years). Variable importance in projection (VIP) of PLSR in combination with multiple linear regression (MLR) was implemented to identify important waveband regions and influential wavelengths related to the measured parameters. The results showed that the PLSR models exhibited moderate to high coefficients of determination (R 2) in both the calibration and validation datasets (0.30-0.95), but that this range of R 2 values depended on parameters and conditions. The PLSR models based on the full spectrum accurately and robustly predicted three of four parameters across all conditions. Based on the combination of PLSR-VIP and MLR analysis, the wavelengths selected within the visible (VIS), red-edge, and middle near-infrared (NIR) wavebands were the most sensitive to all parameters in all conditions, whereas those selected within the shortwave infrared (SWIR) waveband were effective for some parameters in particular conditions. Overall, these results indicated that the PLSR analysis and band selection techniques can offer a rapid and nondestructive alternative approach to accurately estimate growth-and photosynthetic-related trait responses to salinity stress. Salinity and the decreasing availability of freshwater are major factors restricting the productivity of agricultural crops in arid and semiarid regions. In addition, the lack of fresh water available to the agriculture sector in
Successful breeding of plants for salinity stress tolerance requires realistic growing conditions and fast, non-destructive evaluation techniques for phenotypic traits associated with salinity tolerance. In this study, we used subsurface water retention technique (SWRT) as a growing condition and spectral measurements as an evaluation method to assess different agro-morphological traits of salt-tolerant (Sakha 93) and salt-sensitive (Sakha 61) wheat genotypes under three salinity levels (control, 60, and 120 mM NaCl). The effects of salinity on agro-morphological traits were evaluated and related with forty-five published vegetation-and water-spectral reflectance indices (SRIs) taken at both the heading and grain milk growth stages for each salinity level, genotype, and growth stage. In general, the agro-morphological traits gradually decreased as salinity levels increased; however, the reduction in these traits was more pronounced in Sakha 61 than in Sakha 93. The effect of salin-ity levels and their interaction with genotypes on the SRIs was only evident at the grain milk stage. The performance of the spectral reflectance indices depicted that the closest associations with agro-morphological traits depended on salinity level, degree of salt tolerance of the genotypes, and growth stage. The SRI-based vegeta-tive indices correlated better with growth and yield of Sakha 93 than SRI-based water indices and vice versa for Sakha 61. The SRI-based vegetative and water indices are effective for assessment of agro-morphological traits at early growth stages under high salinity level. The functional relationship between grain yield per hectare and the best SRIs was linear for the high salinity level and Sakha 61; however , the quadratic model was found to best fit this relationship for the control, moderate salinity level, and Sakha 93. The overall results indicate that the usefulness of the SRIs for assessment of traits associated with salinity tolerance is limited to salinity level and growth stage. K E Y W O R D S
Sensors
Lettuce is an important vegetable in the human diet and is commonly consumed for salad. It is a source of vitamin A, which plays a vital role in human health. Improvements in lettuce production will be needed to ensure a stable and economically available supply in the future. The influence of nitrogen (N), phosphorus (P), and potassium (K) compounds on the growth dynamics of four hydroponically grown lettuce (Lactuca sativa L.) cultivars (Black Seeded Simpson, Parris Island, Rex RZ, and Tacitus) in tubs and in a nutrient film technique (NFT) system were studied. Hyperspectral images (HSI) were captured at plant harvest. Models developed from the HSI data were used to estimate nutrient levels of leaf tissues by employing principal component analysis (PCA), partial least squares regression (PLSR), multivariate regression, and variable importance projection (VIP) methods. The optimal wavebands were found in six regions, including 390.57–438.02, 497–550, 551–600, 681.34–774, 802–821, an...
2019
Hyperspectral sensing offers a quick and non-destructive alternative for assessing phenotypic parameters of plant physiological status and salt stress tolerance. This study compares the performance of published and modified spectral reflectance indices (SRIs) for estimating and predicting the growth and photosynthetic efficiency of two wheat cultivars exposed to three salinity levels (control, 6.0, and 12.0 dS m −1). Results show that individual SRIs based on visible-and near-infrared (VIS/VIS, NIR/VIS, and NIR/NIR) estimate and predict measured parameters considerably more efficiently than those based on shortwave-infrared (SWIR/VIS and SWIR/NIR), with the exception of some modified indices (the water balance index (WABI-1 (1550, 482) , WABI-2 (1640, 482) , and WABI-3 (1650, 531)), normalized difference moisture index (NDMI (1660, 1742)), and dry matter content index (DMCI (1550, 2305)), which show moderate to strong relationships with measured parameters. Overall results indicate that modified SRIs can serve as rapid and non-destructive high-throughput alternative approaches for tracking growth and photosynthetic efficiency of wheat under salt stress field conditions.
2019
To overcome the salinity threats to crop production in arid conditions, wheat cultivars should be developed with better performance with regard to key physiological traits. Although different chlorophyll fluorescence (ChlF) parameters, such as maximum quantum PSII photochemical efficiency (Fv/Fm), quantum yield of PSII (Φ PSII), and non-photochemical quenching (NPQ) have been proven to be key physiological traits to improve salt tolerance , their evaluation is time-consuming. In this study, hyperspectral canopy reflectance was used to assess ChlF parameters and grain yield (GY) of two wheat cultivars growing in simulated saline field conditions and exposed to three salinity levels (control, 6.0 dS m −1 , and 12.0 dS m −1). Different spectral reflectance indices (SRIs) were formulated as ratios based on contour maps and tested for their relationship with ChlF parameters. The performance of individual SRIs and partial least squares regression (PLSR) models based on ChlF parameters , all examined SRIs, or data fusion of combined ChlF and SRIs to estimate the GY was considered. All examined SRIs failed to assess Φ PSII and NPQ under control condition, but most of them showed a moderate to strong relationship with both parameters under the salinity levels of 6.0 and 12.0 dS m −1. The examined SRIs showed a moderate and strong relationship with Fv/Fm under conditions of 6.0 and 12.0 dS m −1 , respectively. Most SRIs correlated better with the three ChlF parameters for the salt-sensitive cultivar Sakha 61 than for the salt-tolerant cultivar Sakha 93. Several SRIs exhibited strong relationships with GY under the salinity levels of 6.0 and 12.0 dS m −1 and for both cultivars. Overall, the PLSR models exhibited additional improvements for estimating and predicting GY in both calibration and validation datasets over that using individual SRIs. The PLSR model based on data fusion was the best model to accurately estimate GY in the validation model even under control conditions. This study, of a type rarely conducted in simulated saline field conditions, indicates that the ChlF parameters could be linked to hyperspectral reflectance data for the rapid and non-destructive assessment of photosynthetic status and prediction of wheat production under salt stress field conditions.
Advances in Space Research, 2011
The adaptation of specific remote sensing and hyperspectral analysis techniques for the determination of incipient nutrient stress in plants could allow early detection and precision supplementation for remediation, important considerations for minimizing mass of advanced life support systems on space station and long term missions. This experiment was conducted to determine if hyperspectral reflectance could be used to detect nutrient stress in Lactuca sativa L. cv. Black Seeded Simpson. Lettuce seedlings were grown for 90 days in a greenhouse or growth chamber in vermiculite containing modified Hoagland's nutrient solution with key macronutrient elements removed in order to induce a range of nutrient stresses, including nitrogen, phosphorus, potassium, calcium, and magnesium. Leaf tissue nutrient concentrations were compared with corresponding spectral reflectances taken at the end of 90 days. Spectral reflectances varied with growing location, position on the leaf, and nutrient deficiency treatment. Spectral responses of lettuce leaves under macronutrient deficiency conditions showed an increase in reflectance in the red, near red, and infrared wavelength ranges. The data obtained suggest that spectral reflectance shows the potential as a diagnostic tool in predicting nutrient deficiencies in general. Overlapping of spectral signatures makes the use of wavelengths of narrow bandwidths or individual bands for the discrimination of specific nutrient stresses difficult without further data processing.