The discrete element method (DEM), based on particle dynamics, is used to simulate the technological process of soil tillage using agricultural machinery. A key aspect of the DEM for obtaining accurate agrotechnical and energy indicators of soil cultivation is the formulation of particle contact rules, determined by normal and tangential interactions as well as cohesion forces. This study presents a comprehensive analysis of discrete element method (DEM) contact models used to simulate soil cultivation processes. This study addresses a key issue—the absence of a systematic approach to selecting adequate contact models, which limits the accuracy of predicting soil behavior during interaction with agricultural machinery. A detailed classification of 17 combinations of contact models implemented in the commercial software Rocky DEM was performed, grouped into three categories: normal force models (Linear Spring [LSP], Hysteresis [HLS], Hertzian [HSD]), tangential force models (Coulomb, linear spring limit [linear], Mindlin–Deresiewicz), and cohesive force models (linear cohesion [linear], constant force [constant], Johnson–Kendall–Roberts [JKR]). Experimental validation was conducted by analyzing the angle of repose for various soil types (sandy loam, light loam, medium loam, and heavy clay) with moisture contents ranging from 11 to 31%. This analysis identified the nine most effective combinations of contact models to describe normal, tangential, and cohesive forces (LSP–Coulomb–linear, HLS–linear–linear, HLS–Coulomb–linear, HSD–linear–linear, HSD–linear–JKR, HSD–Coulomb–linear, HSD–Coulomb–JKR, HSD–Mindlin–Deresiewicz–linear, HSD–Mindlin–Deresiewicz–JKR), which showed reliable agreement with experimental angle of repose measurements at approximately 85% accuracy. This study significantly contributes to advancing computer modeling methods in agriculture by providing a scientifically grounded approach for selecting DEM contact models.

Производство органической аграрной продукции означает производство в сельском хозяйстве чистых и экономически доступных основной массе населения продуктов питания без вредных для здоровья человека и животных примесей и не ведущих к порче их здоровья. Качество продукции, которую мы употребляем в пищу, зависит от технологии её производства, применения в ней гербицидов и больших доз минеральных удобрений, способствующих возникновению неизлечимых болезней. Современные технологии, исключающие различные виды обработок почвы, не могут существовать без применения гербицидов для уничтожения сорняков. Использование при этом энергонасыщенных тракторов и тяжёлой посевной техники ведет к переуплотнению почвы и снижению водопроницаемости почвы до метрового слоя, исключению влагонакопления, иссушению почвы и к искусственной засухе. В связи с этим коллектив учёных из различных регионов России трудится над разработкой и созданием отечественного проекта исключения продовольственной зависимости России и гарантии здорового жизнеобеспечения народа на основе исследований, проведенных в различных регионах России и одобренных на заседаниях научно-технических советов регионального и федерального уровня. Данный проект предусматривает производство органической продукции питания без применения гербицидов и химикатов на основе органических и биологических удобрений с использованием приемов влагонакопления, применением различных видов обработки почвы агрегатами отечественного...

Introduction. Early diagnosis of diseases of cereal crops is a key task for precision agriculture, because late disease detection results in significant yield losses and inefficient use of plant protection products. Existing phytosanitary control systems mainly focus on offline data post-processing and require substantial computational resources that limits their use on board unmanned aerial vehicles (UAV). In this context, a relevant scientific challenge is to ensure highly accurate real-time plant disease diagnosis under strict computational and energy constraints of onboard platforms.Aim of the Study. The study is aimed at developing an energy-efficient onboard wheat disease detection system capable of real-time operation on embedded platforms.Materials and Methods. The object of the study was wheat crops under open-field conditions. The system was tested on the computer modules Jetson TX2, NavQ Plus and Raspberry Pi 4. To build the system, there was used a modified YOLO-based architecture integrating lightweight convolutional blocks (GhostConv, MBConv), attention modules (SE, CBAM), and an extended BiFPN feature aggregation structure. Training was based on a dataset of 7,500 annotated images of brown and yellow rust symptoms.Results. The developed model demonstrated high detection quality scores: F1-score up to 0.978 and average IoU of 0.82. Performance achieved 16.8 FPS on Jetson TX2 and 13.6 FPS on NavQ Plus, with energy efficiency up to 2.7 FPS/W.Discussion and Conclusion. The comparative analysis showed that the proposed model outperformed the baseline YOLOv5s according to all key metrics. The proposed architecture demonstrates high accuracy, robustness to noise, and real-time applicability. It can be used to create intelligent crop health control systems and automated plant protection control based on UAV platforms.