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The study analyzed three genotypes of industrial hemp, Cannabis sativa L., under the aspect of fiber productivity indices in relation to plant parameters. The study took place in ARDS Lovrin, Romania. The genotypes Silvana (control), Teodora and Lv-300 were evaluated. The experiment took place under the conditions of a chernozem type soil, non-irrigated system. Several parameters and indexes were analyzed: total plant weight (TPW), stem length (SL), inflorescence length (IL), technical length (TL), middle stem diameter (MSD), dry sample weight (DSW), dry fiber content (DFC), pure fiber weight (PFW), and technical fiber (TF). The DFC index showed positive correlations with SL (r=0.708***), with MSD (r=0.712***), with DSW (r=0.739***), with TPW (r=0.546**), and with TL (r=0.399*). The PFW index showed negative correlations with TPW (r=-0.493**) and with DSW (r=- 0.373*). Technical fiber (TF) showed very strong, positive correlations with PFW (r=0.999***) and negative correlations with other parameters (e.g. with TPW, r=-0.493**, with DSW, r=-0.373*). From the comparative analysis of Teodora in relation to Silvana, average values TFT=31.355 (Teodora) and TFS=28.388 (Silvana) resulted, and the differences showed statistical certainty (t=2.5948, with p=0.013; U=72, p=0.0045; p<0.05). From the comparative analysis of Lv-300 in relation to Silvana (control), mean values TFT=29.357 (Lv-300) and TFS=28.388 (Silvana) resulted, but the differences did not show statistical certainty (t=0.763, with p=0.451; U=117, p=0.158; p>0.05). The regression analysis led to equations and graphic models that described the variation of fiber productivity indices in relation to plant biometric parameters. [ABSTRACT FROM AUTHOR]

The aim of the study was to use a technique based on aerial images (UAV) to wheat production estimation. The images were taken with the drone (UAV, DJI Phantom 4) at different image capture heights (ICH), in the range of 1.5 - 50 m from the ground level, and resulted in 18 sets of images (trials, T). The resized images (crop image, 1,000 x 2,000 pixels) were analyzed and the information for RGB color parameters was obtained. Very strong correlations were recorded between color parameters R and G (r=0.993), as well as between these two color parameters and ICH (r=0.940 in the case of R, respectively r=0.918 in the case of G). Based on the trials distribution in the PCA diagram and in the CA dendrogram, 14 trials (T1, T3-T6, T8-T10, T12-T14, T16-T18) were selected for Training & model construction (Tmc), and four trials (T2, T7, T11 and T15) for were used for Testing & model validation (Tmv). Through the regression analysis, a wheat production estimation model was obtained based on RGB parameters under statistical safety conditions (R2=0.999, p<0.001). Flowchart diagram of process was proposed for the present study, and the work process, respectively the obtained models can be adapted for various other agricultural crops. [ABSTRACT FROM AUTHOR]

The study used the technique based on aerial images (UAV) to estimate the production of the sunflower crop, hybrid P64LE25. The experiment was organized within SCDA Lovrin, Timis County, Romania, in the 2021-2022 agricultural year, under the conditions of a chernozem type soil, non-irrigated system. The images were taken with the drone (DJI Phantom 4), at variable heights (H, 3 to 50 m). From the analysis of the digital images, the values of the RGB color parameters were obtained. The distribution of the RGB data series was of normal type (r = 0.950 for R, r = 0.960 for G and r = 0.965 for B). Very strong correlations were recorded between color parameters (r = 0.994 for R and G, r = 0.954 for R and B, r = 0.948 for G and B), and weak correlations between RGB and H. From PCA, PC1 explained 97.698% of variance, and PC2 explained 2.1027% of variance. Regression analysis facilitated the prediction of sunflower production based on RGB values, under statistical safety conditions (p<0.001; RMSEP = 1.7907 in relatin to R and G; RMSEP = 4.5869 in relation to R and B; RMSEP = 3.6978 in relation to G and B). [ABSTRACT FROM AUTHOR]