Model prediction of chlorophyll and fresh biomass in cereal grasses based on aerial images

Constantinescu, Cristian; Herbei, Mihai Valentin; Rujescu, Ciprian; Sala, F.

doi:10.1063/1.5043987

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…The SDI values for all studied plants are presented in table 5. Imaging analysis studies have facilitated the comparative analysis, characterization, detection and classification of some plant species in relation to stress diseases (Lowe et al, 2017;Cheshkova, 2022), some apple varieties (Sala et al, 2017), of of some genotypes in cultivated grasses (Constantinescu et al, 2018). In the case of medicinal plants, favorable results were reported in various studies regarding the analysis and classification of some species under statistical safety conditions (Kan et In the context of the present study, RGB color parameters and some calculated ratios were identified as elements that were associated with the species of aromatic and medicinal plants studied, and can be considered as useful elements in the analysis and identification of these species in vegetation stages.…”

Section: Resultsmentioning

confidence: 99%

Characterization of Some Aromatic and Medicinal Plants Species Based on Uav Images

BUZNA,

HORABLAGA,

SABO

et al. 2023

LSSD

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The study used imaging analysis to characterize several species of aromatic and medicinal plants. UAV images were taken on five aromatic and medicinal plants species, cultivated by SCDA Lovrin, Timis county, Romania. The biological material was represented by Lavandulua x intermedia (Lavandin), P1; Hyssopus officinalis L., P2, P6; Origanum vulgare L., P3; Thymus serpyllum L., P4; Salvia officinalis L., P5 (P1 to P6 species code in the article). The images were analyzed (four repetitions) and the values of the RGB color parameters were obtained. The ANOVA test confirmed the presence of variance and the safety of the data (F>F crit, p<0.001; Alpha=0.001). The correlation analysis highlighted positive and negative correlations of different intensity levels between RGB parameters and calculated ratios (R/G, R/B, G/R, G/B, B/R, B/G). The variation of G in relation to R was described by a polynomial equation of degree 3 (R2=0.956), and the variation of B in relation to R was described by a polynomial equation of degree 2 (R2=0.736). High variability was recorded in the case of parameter B (CVB=23.44534), plant P3 (Origanum vulgare L.), and low variability was recorded in the case of color parameter G (CVG=2.66030), plant P2 (Hyssopus officinalis L.). Based on PCA, the distribution diagram of plant species in relation to RGB color parameters and calculated ratios, as biplot, was obtained. PC1 explained 66.037% of variance, and PC2 explained 33.095% of variance in relation to RGB, and PC1 explained 58.923% of variance, and PC2 explained 36.46% of variance in relation to the calculated ratios. The cluster analysis facilitated the grouping of plant species (P1 to P6) based on similarity in relation to the RGB color parameter values (Coph.corr. = 0.959).

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Section: Resultsmentioning

confidence: 99%

Characterization of Some Aromatic and Medicinal Plants Species Based on Uav Images

BUZNA,

HORABLAGA,

SABO

et al. 2023

LSSD

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show abstract

“…In wheat crop, imaging analysis based on different types of images (satellite, aerial or terrestrial) was frequently used to analyze and characterize the state of plant vegetation in relation to soil, climatic and technological conditions (Constantinescu et al, 2018;Sala et al, 2020;Cheng et al, 2022) The estimation of wheat production based on remote sensing techniques has also been the subject of numerous studies, which have developed statistical safety estimation models (Aranguren et al, 2020;Cheng et al, 2022;Saad El Imanni et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Wheat Crop and Production Estimation Based on Remote Sensing

HERBEI,

JURJESCU,

SALA

2023

LSSD

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Based on remote sensing, the study analyzed the dynamics of a wheat crop using regression analysis to estimate production in relation to the NDMI, NDVI, MSAVI and NBR indices. The wheat crop considered in the study, was located in the area of Sacalaz, Timis county, Romania. Seven series of satellite images were taken, between February and June 2021. Based on the spectral information, the NDMI, NDVI, MSAVI and NBR indices were calculated and series of 595 values were obtained for each index. Very strong correlations were recorded between NBR and NDMI (r=0.999***), between MSAVI and NDVI (r=0.994***), between NBR and MSAVI (r=0.919**), between NDVI and NDMI ( r=0.911) and between NBR and NDVI (r=0.909**). Polynomial equations described the variation of NBR in relation to NDMI (R2=0.987), in relation to MSAVI (R2=0.794) and in relation to NDVI (R2=0.795). The variation of indices in relation to time, during the study period, was described by polynomial equations of the 2nd and 3rd degree, under statistical accuracy conditions (R2=0.854 for NDMI; R2=0.919 for NDVI; R2=0.956 for MSAVI, and R2=0.873 for NBR). Through the regression analysis, the wheat production was predicted, based on the calculated indices, and in different combinations of indices. The highest level of accuracy in the prediction of wheat production was recorded in the case of using the NDMI and NBR indices (p<0.001, RMSPE=0.24847), followed by the analysis variant in which the NDVI and MSAVI indices were considered (p=0.0002, RMSEP=1.59084) and the analysis variant in which NDVI and NBR indices were used (p=0.00081, RMSEP=5.20218). Graphical models, in 3D format and in the form of isoquants, have described the variation of wheat production in relation to the NDMI, NDVI, MSAVI and NBR indices.

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“…Some studies on grass cereals have correlated chlorophyll content (determined by non-destructive methods) and fresh biomass with aerial images (UAV images) to characterize different plant genotypes and generate prediction models through image analysis (Constantinescu et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Nitrogen Variable Rate Influence on Photosynthetic Pigments in Wheat Flag Leaf

GHERBAN,

SALA

2023

LSSD

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The study evaluated the variation of photosynthetic pigments (chlorophyll, carotenoids) in wheat flag leaves in relation to the nitrogen variation rate. The PG102 winter wheat variety was cultivated, in non-irrigated conditions, on a chernozem type soil. Nitrogen was applied in doses of 0 - 280 kg ha-1 a.s., in a variation rate of 40 kg ha-1 a.s. (active substances). The field experiments took place in ULS "King Mihai I" from Timisoara, Romania. At the time of flowering (BBCH code 6), random samples of flag leaves were taken on each experimental variant. The chlorophyll content (Chl) varied between Chl = 43.73±1.04 (V1) and Chl = 58.92±1.21 (V6). The carotenoid content (Car) varied between Car = 10.12±0.59 (V1) and Car = 21.56±0.49 (V6). Based on the Chl and Car values, the Chl/Car and Car/Chl ratios were calculated for each experimental variant. According to Kruskal-Wallis test, resulted significant difference between sample medians (p (same) = 1.56E-05 in case of Chl data series; p (same) = 2.616E-09 in case of Car data series). The variation of Chl and Car pigments in relation to nitrogen (N) was described by polynomial equations of the 3rd degree, under statistical safety conditions (R2 = 0.981 in the case of Chl; R2 = 0.986 in the case of Car; p<0.001). The variation of Car in relation to Chl was described by a polynomial equation of degree 3 (R2 = 0.978, p<0.001).

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Model prediction of chlorophyll and fresh biomass in cereal grasses based on aerial images

Cited by 4 publications

References 15 publications

Characterization of Some Aromatic and Medicinal Plants Species Based on Uav Images

Characterization of Some Aromatic and Medicinal Plants Species Based on Uav Images

Evaluation of the Wheat Crop and Production Estimation Based on Remote Sensing

Nitrogen Variable Rate Influence on Photosynthetic Pigments in Wheat Flag Leaf

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