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Sirvi Autor "Hristova, G." järgi

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  • Pisipilt
    Kirje
    Impact of the CEMOS AUTOMATIC intelligent system on the field performance and energy efficiency of a CLAAS LEXION 770 combine harvester
    (Estonian University of Life Sciences, 2026) Hristova, G.; Veleva, P.; Patev, T.
    Efficient management of combine harvesters is a key factor in modern precision agriculture, where automation and intelligent control systems play an essential role in optimizing operational performance and energy efficiency. This study presents a comparative analysis of the CLAAS LEXION 770 combine harvester operating with and without the CEMOS AUTOMATIC intelligent optimization system during wheat harvesting (Avenue variety). Field experiments were conducted to evaluate key performance indicators, including grain losses, fuel consumption, engine load, and throughput capacity under real harvesting conditions. The results show that the use of the CEMOS AUTOMATIC system improved fuel efficiency by 7–10%, reduced grain losses by 15–20%, and provided more stable machine operation compared with manual control. Furthermore, the intelligent control algorithm optimized the settings of threshing and cleaning systems in real time, resulting in improved productivity and reduced operator workload. The findings confirm that the integration of automated optimization systems such as CEMOS AUTOMATIC significantly enhances the energy efficiency and sustainability of modern harvesting operations.
  • Pisipilt
    Kirje
    Relationships between air relative humidity and soil temperature in open-field tomato production
    (Estonian University of Life Sciences, 2026) Dallev, M.; Hristova, G.
    Understanding microclimatic interactions between atmospheric and soil parameters is essential for precision agriculture and irrigation management. The present study evaluated the relationships between air temperature, relative humidity, soil temperature, and soil moisture under open-field tomato production using a portable ESP32-based monitoring system. Measurements were conducted at ten field points with three replicates per point (n = 30). Descriptive statistics indicated relatively stable environmental conditions during the observation period. Pearson correlation analysis revealed a statistically significant inverse relationship between air relative humidity and soil temperature (r = -0.376, p = 0.040), while no significant linear relationships were observed between the remaining variables (p > 0.05). Linear regression analysis confirmed that increasing air relative humidity was associated with decreasing soil temperature (R² = 0.142, p = 0.040), although the explanatory power of the model remained limited. Validation against a Meteobot® reference weather station demonstrated high accuracy of soil temperature measurements (relative error 0.74%) and acceptable performance for soil moisture (6.28%). The results indicate that low-cost IoT-based monitoring systems can reliably capture microclimatic interactions under field conditions and support their application in precision agriculture.