Many images of agricultural crops are made at different times of the day, images with different spectral information about the same crop in relation to conditions when the picture was taken. A set of 30 digital images of a wheat crop in the BBCH 3-Stem elongation code 32–33 stage was captured between 9 am and 14 (UTC+3), in the 0°–180° variation range of the image acquisition angle on the E-W axis (cardinal directions). A high variation of the spectral data given by the combination of the hour (h) and angle (a) at which the images were captured was found. The interdependence relationship between the analyzed parameters (r, g, and b), and the time (t) and the angle (a) of image acquisition was assessed with the linear correlation coefficient. By calculating the roots of the mathematical expressions of the correlation coefficients dependence on the angles (a) or times of day (t), the optimal angle and time were determined as a combination of the two variables for capturing images and obtaining optimal r_o, g_o, b_o values. The correction coefficients of the normalized r, g, and b values obtained out of the optimal field were determined. To this end, the multiplication of the r(a,t), g(a,t), and b(a,t) values with the ρ_a,t, γ_a,t, and β_a,t correction coefficients was suggested to reach the optimal values for sustainable decisions.

Monitoring of crops by satellite techniques is very useful in the context of precision agriculture, regarding crops management and agricultural production. The present study has evaluated the interrelationship between maize biomass production and satellite indices (NDVI and NDBR) during five development stages (BBCH code), highlighting different levels of correlation. Biomass production recorded was between 2.39±0.005 t ha−1 (12-13 BBCH code) and 51.92±0.028 t ha−1 (83-85 BBCH code), in relation to vegetation stages studied. Values of chlorophyll content ranged from 24.1±0.25 SPAD unit (12-13 BBCH code) to 58.63±0.47 SPAD unit (71-73 BBCH code), and the obtained satellite indices ranged from 0.035641±0.002 and 0.320839±0.002 for NDVI indices respectively 0.035095±0.034 and 0.491038±0.018 in the case of NDBR indices. By regression analysis it was possible to obtain predictive models of biomass in maize based on the satellite indices, in statistical accurate conditions. The most accurate prediction was possible based on NDBR index (R2 = 0.986, F = 144.23, p<0.001, RMSE = 1.446), then based on chlorophyll content (R2 = 0.834, F = 16.14, p = 0.012, RMSE = 6.927) and NDVI index (R2 = 0.682, F = 3.869, p = 0.116, RMSE = 12.178).

In the case of foliar area studies on vines, with a large number of determinations, a simple, fast, sufficiently accurate and low-cost method is very useful. The typology of leaves on the vine is complex, characterized by several descriptive parameters: median rib; secondary venations of the first and second order; angles between the median rib and the secondary venations; sinuses; length and width of the leaf. The present study aimed to evaluate models for calculating the leaf area based on descriptive parameters and KA (KA as the surface constant used to calculate the leaf area) for six vine cultivars, ‘Cabernet Sauvignon’ (CS), ‘Muscat Iantarnîi’ (MI), ‘Muscat Ottonel’ (MO), ‘Chasselas’ (Ch), ‘Victoria’ (Vi) and ‘Muscat Hamburg’ (MH). The determined KA surface constants had subunit values (0.91 to 0.97), except for the cultivars ‘Muscat Iantarnîi’ and ‘Muscat Ottonel’ where the surface constant KA₂ (in relation to the second-order secondary venations) had supraunitary values (1.07 and 1.08, respectively). The determination of the leaf area was possible under different conditions of statistical accuracy (R² = 0.477, p = 0.0119, up to R² = 0.988, p < 0.001) in relation to the variety and parametric descriptors considered. The models obtained from the regression analysis facilitated a more reliable prediction of the leaf area based on the elements on the left side of the leaf, in relation to the median rib, compared to those on the right. The accuracy of the results was checked on the basis of minimum error (ME) and confirmed by parameters R², p and RMSE.

The purpose of the study was to characterize the 18 varieties of cereals (wheat, barley, triticale) and evaluation of a model to estimate the chlorophyll content and fresh biomass on the basis of aerial images. For this, from digital images taken with a drone (DJI Phantom) were extracted the spectral data in RGB system. On their basis, have been calculated the normalized values (rgb), and the values of the HSB system, and subsequently were been determined the indices INT, NDI and DGCI. The content of chlorophyll (Chl) correlated to a higher level with the DGCI index (r = 0.846, p<0.01, F = 40.21), and with fresh biomass (r = 0.772, p<0.01, F = 23.60). Analyzing various types of regression functions (polynomial functions of grade I, II or III, hyperbolic, or exponential functions) describing the relationship between DGCI and Chl, the correlation coefficient values were approximately equal (0.84) and the Sigf values being in all cases less than 0.001.