Bio-priming is a new technique of seed treatment that improves seed germination, vigor, crop growth and yield. The objective of this study was to evaluate the effectiveness of Bradyrhizobium japonicum (commercial strains) and Bacillus megaterium (newly isolated strains) as a single inoculant and co-inoculant during seed bio-priming to improve seed germination and initial seedling growth of two soybean cultivars. The treated seeds were subjected to germination test (GT), cold test (CT) and accelerated aging test (AAT). B. megaterium significantly improved all parameters in GT and CT; final germination, shoot length, root length, root dry weight, and seedling vigor index in AAT, as compared to control. In addition, co-inoculation significantly increased all parameters except shoot dry weight in GT; all parameters in CT; germination energy, shoot length, root length, and seedling vigor index in AAT, in comparison to the control. Moreover, Br. japonicum significantly improved the germination energy, shoot length, shoot dry weight, root dry weight, and seedling vigor index in GT; all parameters in CT; shoot length, root length, and seedling vigor index in AAT, compared with non-primed seeds. Thus, B. megaterium strains could be used in soybean bio-priming as a potential single inoculant and co-inoculant, following proper field evaluation.

Soybean plant density is an important factor of successful agricultural production. Due to the high number of plants per unit area, early plant overlapping and eventual plant loss, the estimation of soybean plant density in the later stages of development should enable the determination of the final plant number and reflect the state of the harvest. In order to assess soybean plant density in a digital, nondestructive, and less intense way, analysis was performed on RGB images (containing three channels: RED, GREEN, and BLUE) taken with a UAV (Unmanned Aerial Vehicle) on 66 experimental plots in 2018, and 200 experimental plots in 2019. Mean values of the R, G, and B channels were extracted for each plot, then vegetation indices (VIs) were calculated and used as predictors for the machine learning model (MLM). The model was calibrated in 2018 and validated in 2019. For validation purposes, the predicted values for the 200 experimental plots were compared with the real number of plants per unit area (m²). Model validation resulted in the correlation coefficient—R = 0.87, mean absolute error (MAE) = 6.24, and root mean square error (RMSE) = 7.47. The results of the research indicate the possibility of using the MLM, based on simple values of VIs, for the prediction of plant density in agriculture without using human labor.

Seed decay is one of the most important diseases of soybean (Glycine max (L.) Merr.) which can greatly affect seed quality. Some of the most damaging causal agents of soybean seed decay are Diaporthe longicolla, D. sojae, D. caulivora, and D. aspalathi (Li, 2011). A total of 94 single-conidial isolates were obtained from soybean seeds collected throughout the soybean-producing area in Serbia from 1996 to 2015. Isolates were separated into two groups (A and B) according to their morphological features. In group A, 70 isolates were assigned with the following morphological characteristics: white mycelium, black stromatic structures, pycnidial conidiomata with alpha conidia and rarely beta conidia, and absence of perithecia (Hobbs et al. 1985). Twenty-four isolates were assigned to group B with culture characteristics that included white mycelium, pycnidial conidiomata with alpha and beta conidia, and perithecia (Udayanga et al. 2015). Using sequence comparison of the internal transcribed spacer (ITS1-5.8S-I...