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.

Diaporthe seed decay can compromise seed quality in soybean [Glycine max (L.) Merr.] in the warm and humid production areas of the United States during crop maturation. In the current study, 45 isolates of Diaporthe were recovered from seed sampled from soybean fields affected by Diaporthe-associated diseases in eight U.S. states in 2017. The isolates obtained belonged to 10 species of Diaporthe based on morphology and phylogenetic analyses of the internal transcribed spacer, partial translation elongation factor 1-α, and β-tubulin gene sequences. The associated species included D. aspalathi, D. caulivora, D. kongii, D. longicolla, D. sojae, D. ueckerae, D. unshiuensis, and three novel fungi, D. bacilloides, D. flavescens, and D. insulistroma. One isolate each of the 10 species was examined for pathogenicity on seed of cultivar Sava under controlled conditions. Seven days postinoculation, significant differences in the percentages of decayed seeds and seedling necrosis were observed among the isolates and the noninoculated control (P < 0.0001). While the isolates of D. bacilloides, D. longicolla, and D. ueckerae caused a significantly greater percentage of decayed seeds (P < 0.0001), the isolate of D. aspalathi caused the greatest seedling necrosis (P < 0.0001). The observation of new fungi causing Diaporthe seed decay suggests the need for a more comprehensive survey in U.S. soybean producing areas since members of the genus Diaporthe appear to form a complex that causes seed decay.

The Fusarium genus includes some of the most economically and ecologically impactful fungal pathogens affecting global agriculture and human health. Over the past 15 years, rapid advances in molecular biology, genomics, and diagnostic technologies have reshaped our understanding of Fusarium taxonomy, host–pathogen dynamics, mycotoxin biosynthesis, and disease management. This review synthesizes key developments in these areas, focusing on agriculturally important Fusarium species complexes such as the Fusarium oxysporum species complex (FOSC), Fusarium graminearum species complex (FGSC), and a discussion on emerging lineages such as Neocosmospora. We explore recent shifts in species delimitation, functional genomics, and the molecular architecture of pathogenicity. In addition, we examine the global burden of Fusarium-induced mycotoxins by examining their prevalence in three of the world’s most widely consumed staple crops: maize, wheat, and rice. Last, we also evaluate contemporary management strategies, including molecular diagnostics, host resistance, and integrated disease control, positioning this review as a roadmap for future research and practical solutions in Fusarium-related disease and mycotoxin management. By weaving together morphological insights and cutting-edge multi-omics tools, this review captures the transition into a new era of Fusarium research where integrated, high-resolution approaches are transforming diagnosis, classification, and management.

Modern agriculture necessitates the use of techniques and tools that pollute the environment less and improve the safety of food and feed production. In the field of plant protection, drones are attracting increasing attention due to their versatility and applicability in a variety of environmental and working conditions. Drone crop spraying techniques offer several advantages, including increased safety and cost effectiveness through autonomous and programmed operations based on specific schedules and routes. One of the main advantages of using drones for plant protection is their ability to monitor large areas of crops in a short amount of time. In addition to crop protection management, using drones for augmentative biocontrol facilitates the distribution of beneficial organisms to the exact locations where they are required, which can increase the effectiveness of biocontrol agents while reducing distribution costs. In this context, given the very limited commercial use of drones in the Western Balkans’ agri-food sector, the use of drones in the agri-food industry is a topic that needs to be elaborated on and highly promoted. Additionally, the specific legal regulations in Serbia that currently limit the use of drones in agriculture must be outlined. Conventional crop production is still significantly more prevalent in Serbia, but given the region’s continuous technological progress, there is no doubt that farmers’ education and future investments in precision agriculture will most likely increase the use of state-of-the-art technologies and drones in agriculture.

One hundred fifty-two Diaporthe isolates were recovered from symptomatic soybean (Glycine max) stems sampled from the U.S. states of Iowa, Indiana, Kentucky, Michigan, and South Dakota. Using morphology and DNA sequencing, isolates were identified as D. aspalathi (8.6%), D. caulivora (24.3%), and D. longicolla (67.1%). Aggressiveness of five isolates each of the three pathogens was studied on cultivars Hawkeye (D. caulivora and D. longicolla) and Bragg (D. aspalathi) using toothpick, stem-wound, mycelium contact, and spore injection inoculation methods in the greenhouse. For D. aspalathi, methods significantly affected disease severity (P < 0.001) and pathogen recovery (P < 0.001). The relative treatment effects (RTE) of stem-wound and toothpick methods were significantly greater than for the other methods. For D. caulivora and D. longicolla, a significant isolate × method interaction affected disease severity (P < 0.05) and pathogen recovery (P < 0.001). Significant differences in RTEs were observed among D. caulivora and D. longicolla isolates only when the stem-wound and toothpick methods were used. Our study has determined that the stem-wound and toothpick methods are reliable to evaluate the three pathogens; however, the significant isolate × method interactions for D. caulivora and D. longicolla indicate that multiple isolates should also be considered for future pathogenicity studies.

Soybean time of flowering and maturity are genetically controlled by E genes. Different allelic combinations of these genes determine soybean adaptation to a specific latitude. The paper describes the first attempt to assess adaptation of soybean genotypes developed and realized at Institute of Field and Vegetable Crops, Novi Sad, Serbia [Novi Sad (NS) varieties and breeding lines] based on E gene variation, as well as to comparatively assess E gene variation in North-American (NA), Chinese, and European genotypes, as most of the studies published so far deal with North-American and Chinese cultivars and breeding material. Allelic variation and distribution of the major maturity genes (E1, E2, E3, and E4) has been determined in 445 genotypes from soybean collections of NA ancestral lines, Chinese germplasm, and European varieties, as well as NS varieties and breeding lines. The study showed that allelic combinations of E1–E4 genes significantly determined the adaptation of varieties to different geographical regions, although they have different impacts on maturity. In general, each collection had one major E genotype haplogroup, comprising over 50% of the lines. The exceptions were European varieties that had two predominant haplogroups and NA ancestral lines distributed almost evenly among several haplogroups. As e1-as/e2/E3/E4 was the most common genotype in NS population, present in the best-performing genotypes in terms of yield, this specific allele combination was proposed as the optimal combination for the environments of Central-Eastern Europe.