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.

Background: Biomass accumulation as a growth indicator can be significant in achieving high and stable soybean yields. More robust genotypes have a better potential for exploiting available resources such as water or sunlight. Biomass data implemented as a new trait in soybean breeding programs could be beneficial in the selection of varieties that are more competitive against weeds and have better radiation use efficiency. The standard techniques for biomass determination are invasive, inefficient, and restricted to one-time point per plot. Machine learning models (MLMs) based on the multispectral (MS) images were created so as to overcome these issues and provide a non-destructive, fast, and accurate tool for in-season estimation of soybean fresh biomass (FB). The MS photos were taken during two growing seasons of 10 soybean varieties, using six-sensor digital camera mounted on the unmanned aerial vehicle (UAV). For model calibration, canopy cover (CC), plant height (PH), and 31 vegetation index (VI) were extracted from the images and used as predictors in the random forest (RF) and partial least squares regression (PLSR) algorithm. To create a more efficient model, highly correlated VIs were excluded and only the triangular greenness index (TGI) and green chlorophyll index (GCI) remained. Results: More precise results with a lower mean absolute error (MAE) were obtained with RF (MAE = 0.17 kg/m²) compared to the PLSR (MAE = 0.20 kg/m²). High accuracy in the prediction of soybean FB was achieved using only four predictors (CC, PH and two VIs). The selected model was additionally tested in a two-year trial on an independent set of soybean genotypes in drought simulation environments. The results showed that soybean grown under drought conditions accumulated less biomass than the control, which was expected due to the limited resources. Conclusion: The research proved that soybean FB could be successfully predicted using UAV photos and MLM. The filtration of highly correlated variables reduced the final number of predictors, improving the efficiency of remote biomass estimation. The additional testing conducted in the independent environment proved that model is capable to distinguish different values of soybean FB as a consequence of drought. Assessed variability in FB indicates the robustness and effectiveness of the proposed model, as a novel tool for the non-destructive estimation of soybean FB.

To overcome the different challenges to food security caused by a growing population and climate change, soybean (Glycine max (L.) Merr.) breeders are creating novel cultivars that have the potential to improve productivity while maintaining environmental sustainability. Genomic selection (GS) is an advanced approach that may accelerate the rate of genetic gain in breeding using genome-wide molecular markers. The accuracy of genomic selection can be affected by trait architecture and heritability, marker density, linkage disequilibrium, statistical models, and training set. The selection of a minimal and optimal marker set with high prediction accuracy can lower genotyping costs, computational time, and multicollinearity. Selective phenotyping could reduce the number of genotypes tested in the field while preserving the genetic diversity of the initial population. This study aimed to evaluate different methods of selective genotyping and phenotyping on the accuracy of genomic prediction for soybean yield. The evaluation was performed on three populations: recombinant inbred lines, multifamily diverse lines, and germplasm collection. Strategies adopted for marker selection were as follows: SNP (single nucleotide polymorphism) pruning, estimation of marker effects, randomly selected markers, and genome-wide association study. Reduction of the number of genotypes was performed by selecting a core set from the initial population based on marker data, yet maintaining the original population’s genetic diversity. Prediction ability using all markers and genotypes was different among examined populations. The subsets obtained by the model-based strategy can be considered the most suitable for marker selection for all populations. The selective phenotyping based on makers in all cases had higher values of prediction ability compared to minimal values of prediction ability of multiple cycles of random selection, with the highest values of prediction obtained using AN approach and 75% population size. The obtained results indicate that selective genotyping and phenotyping hold great potential and can be integrated as tools for improving or retaining selection accuracy by reducing genotyping or phenotyping costs for genomic selection.

The non-destructive estimation of grain yield could increase the efficiency of soybean breeding through early genotype testing, allowing for more precise selection of superior varieties. High-throughput phenotyping (HTPP) data can be combined with machine learning (ML) to develop accurate prediction models. In this study, an unmanned aerial vehicle (UAV) equipped with a multispectral camera was utilized to collect data on plant density (PD), plant height (PH), canopy cover (CC), biomass (BM), and various vegetation indices (VIs) from different stages of soybean development. These traits were used within random forest (RF) and partial least squares regression (PLSR) algorithms to develop models for soybean yield estimation. The initial RF model produced more accurate results, as it had a smaller error between actual and predicted yield compared with the PLSR model. To increase the efficiency of the RF model and optimize the data collection process, the number of predictors was gradually decreased by eliminating highly correlated VIs and selecting the most important variables. The final prediction was based only on several VIs calculated from a few mid-soybean stages. Although the reduction in the number of predictors increased the yield estimation error to some extent, the R² in the final model remained high (R² = 0.79). Therefore, the proposed ML model based on specific HTPP variables represents an optimal balance between efficiency and prediction accuracy for in-season soybean yield estimation.

Summary: The objective of this study, conducted in the northern Serbian province of Vojvodina, was to analyze the effect of surface and subsurface drip irrigation (with drip lateral placement depths of 0.05 and 0.1 m) on the yield and water productivity of onions (Allium cepa L., var. ‘Holandski žuti’). The irrigation applied was scheduled on the basis of the water balance method. The daily evapotranspiration rate was computed using the reference evapotranspiration (ET _o ) based on the Hargreaves equation and the crop coefficient (kc). The irrigation rate was 30 mm, whereas the amount of water added by irrigation during the season was 150 mm. According to the results obtained, the onion yield under irrigated conditions was significantly higher than that under non-irrigated (control) conditions. Differences in the yield obtained using surface and subsurface irrigation were non-significant. The amounts of water used for evapotranspiration under irrigated and non-irrigated conditions were 363 mm and 220 mm, respectively. The value of the surface irrigation yield response factor (K _y ) was 0.62, whereas the values of the subsurface irrigation yield response factor (K _y ) were 0.61 (0.05 m) and 0.79 (0.1 m). Consequently, onions grown from sets proved moderately sensitive to water stress under regional climate conditions and could be grown without irrigation. The value of the irrigation water use efficiency (I _wue ) ranged from 3.55 to 4.97 kg m ⁻³ , whereas the value of the evapotranspiration water use efficiency (ET _wue ) ranged from 3.72 to 5.22 kg m ⁻³ . The highest yield of onions was obtained using a drip lateral placement depth of 0.1 m, which is recommended for high-yielding onion production.

In order to examine the influence of inter-row cultivation on the soybean yield, a trial was set up in the Agricultural School in Backa Topola from 2015 to 2017. Three soybean varieties were selected at the Institute of Field and Vegetable Crops in Novi Sad. Galina, Sava and Rubin were used. The trials included the different number of inter-row cultivations: control - without inter-row cultivation, one inter-row cultivation, two and three inter-row cultivations of soybean crops. Obtained results showed that an average yield of soybean increased with an increase in the number of inter-row cultivations during the soybean vegetation period. With one inter-row cultivation, the yield increase was 5.33%, with two inter-row cultivations - 7.10% and with three cultivations - 7.33%. In the years with a lower precipitation, the influence of intra-row cultivation was much more effective. In 2017, the increase ranged from 10.33% to 13.62%, and in 2016 from 3.09% to 4.27%. Yield was increased due to reduced evapotranspiration of soil moisture as well as prevention of descending of water into deeper layers. During summer, this allowed better water conservation in the soil for critical periods, and also increased the activity of microorganisms as well as more intensive mineralization of organic matter, which ultimately influenced the yield.