No abstract

SUMMARYThe goal of the paper is to compare the impact of conventional tillage and no-tillage technology on the growth, the yield and yield components of sweet corn, cultivated on chromic luvisols. A field experiment with Super Sweet 71,12 R hybrid was carried out in 2014 in the region of Sofia, Bulgaria. The impact of both systems on the total fresh ear yield, marketable fresh ear yield, total ear number, marketable ear number, single marketable fresh ear mass, marketable ear row number, one row kernel number of a marketable ear, marketable fresh ear kernel mass, plant height, leaf number per plant, ear legth, and tassel length was established. Analysis of variance was applied to all data obtained. The experiment was carried out on chromic luvisols, in a temperate-continental climate and in a very humid year. The results showed that the conventional tillage in such nature conditions have had better performance than the no-tillage technology. The yield of marketable fresh ears under conventional tillage was twice higher than that under no-tillage, i.e. 8.5 Mg/ha vs. 4.2 Mg/ha; kernel mass of a single fresh ear was with 22.6% higher, i.e. 163.8 g vs. 133.6 g, the 1000-kernel mass was with 14.4% higher, i.e. 337.2 g vs. 293.0 g. Analogously, the plants were longer and had thicker stems with greater leaf number, resulting in 12.5% greater fresh-ear length -20.7 cm. The total fresh biomass under conventional tillage reaches 633.0 g/plant vs. 414.6 g/plant under no-tillage and the dry matter -145.6 g vs. 103.7 g/plant. The protein content was 13.8% vs. 12.7%. The production under conventional tillage was more profitable. The price of a marketable corn ear was much lower -0.0358 EUR/pc vs. 0.0512 EUR/pc. No-till requires precise preliminary estimation of the nature conditions and weather prognoses and cannot be recommended to very humid areas and

No abstract

Artificial Intelligence brings exciting innovations in all aspects of life and creates new opportunities across industry sectors. At the same time, it raises significant questions in terms of trust, ethics, and accountability. This paper offers an introduction to the AI4Media project, which aims to build on recent advances of AI in order to offer innovative tools to the media sector. AI4Media unifies the fragmented landscape of media-related AI technologies by investigating new learning paradigms and distributed AI, exploring issues of AI explainability, robustness and privacy, examining AI techniques for content analysis, and exploiting AI to address major societal challenges. In this paper, we focus on our vision of how such AI technologies can reshape the media sector, by discussing seven industrial use cases that range from combating disinformation in social media and supporting journalists for news story creation, to high quality video production, game design, and artistic co-creation. For each of these use cases, we highlight the present challenges and needs, and explain how they can be efficiently addressed by using innovative AI-driven solutions.

No abstract