Agriculture

UAV-based rice direct seeding offers high operational efficiency and reduced labor demand, yet seed distribution uniformity remains a major limitation for centrifugal spreading devices. This study aims to design and optimize a novel centrifugal drone rice direct seeding device to improve seed lateral distribution uniformity. In this study, a centrifugal drone rice direct seeding device was developed with a concave perforated disc and double-arc seed-pushing blades to regulate seed motion and improve lateral distribution uniformity. Discrete element method (DEM) simulations were conducted to examine the effects of disc tilt angle, blade type, and blade number. Single-factor and response-surface simulation results identified an optimal parameter combination of a 29.0° disc tilt angle, double-arc blades with a 110° arc angle, and six blades. Based on these results, the disc structure was further refined, and the simulated lateral coefficient of variation (CV) of seed distribution reached 18.22%. Bench tests yielded a minimum CV of 16.34%, an average CV of 19.36%, and a total discharge coefficient of variation of 0.276%, which agrees with the simulation outcomes and supports the validity of the DEM model. Overall, the proposed device demonstrates improved seeding uniformity and meets agronomic requirements for rice cultivation, offering farmers a high-efficiency planting solution and providing UAV manufacturers with a validated double-arc disc design for equipment optimization.

Silage additives formulated with lactic acid bacteria (LAB) are commonly applied to enhance fermentation efficiency and aerobic stability. However, comparative evaluations across different forage species are still scarce. This in vitro experiment assessed the influence of eleven commercial silage inoculants containing various combinations of homo- and heterofermentative LAB on fermentation dynamics, nutrient conservation, and aerobic stability of medium-wilted alfalfa (Medicago sativa L.), perennial ryegrass (Lolium perenne L.), and red clover/perennial ryegrass silages. Experimental silages were prepared in 3 L laboratory silos and stored for 90 days. All inoculated treatments exhibited significantly lower pH values at both 3 and 90 days of ensiling compared with the untreated control (p < 0.05). LAB application increased the concentration of total fermentation acids and lactic acid in all forage types, although responses varied depending on inoculant composition. Inoculants containing Lentilactobacilllus buchneri produced the greatest acetic acid concentrations and resulted in a marked enhancement of aerobic stability. Compared with the control, silage inoculation significantly decreased dry matter losses by 35–64% and ammonia-N proportion by 20–37%, leading to an additional dry matter recovery of 1.29–2.87%. Control silages showed the lowest aerobic stability (97.2 h), while inoculated silages ranged from 126.0 to 200.4 h, with the extent of improvement differing among forage species and LAB formulations. In conclusion, commercial silage inoculants incorporating diverse LAB strains effectively improve fermentation quality, limit nutrient degradation, and enhance aerobic stability of legume and grass silages under controlled experimental conditions.

The advent of smart farms, enabled by information and communication technologies (ICT) and the Internet of Things (IoT), has improved productivity and sustainable agriculture. However, the large-scale implementation of smart farms is currently hampered by physical constraints. These constraints have led to the concept of open-field smart farming as a viable alternative. In this paradigm, data from unmanned aerial vehicles (UAVs) play a central role in effective and sustainable agricultural management. The quantitative analysis of such data requires highly reliable technological solutions. The objective of this study is to conduct a comparative analysis of image binarization algorithms for UAV-based soybean canopy extraction across growth stages and to contribute to the development of an image labeling methodology. UAVs were used to capture images of soybean fields at different growth stages, and a comparative analysis was performed using binarization image algorithms. The performance of each algorithm was evaluated using Normalized Cross Correlation (NCC) and Mean Absolute Error (MAE). The results indicate that the Excess Green (ExG) and Excess Green minus Excess Red (ExGR) vegetation indices provide accurate and stable soybean canopy extraction across growth stages when combined with Adaptive and Otsu binarization algorithms. These indices are particularly suitable for extracting soybean canopy from UAV-based data, thereby expanding the scope of precision analysis in the agricultural sector and providing data for advancing precision agriculture technology. This study contributes to the standardization and efficient use of UAV-based agricultural data processing. However, since manual weeding was performed prior to image acquisition to ensure that only soybean plants were present, reflecting standard agricultural practices in South Korea, additional validation would be required for application in fields where weeds are naturally present.