Active learning and teaching today aim to actively involve all participants in the colaborative interaction. So far, the students were passive listeners who are just absorbing information from teaching materials and teachers. They were not included in collection of information, commenting and analysis. In active learning, the role of students is significantly changed because they need to take responsibility for their learning. Active learning is learning that encourages and stimulates the development of thinking by adopting reallife situations, as well as imaginary situation in simulated area. Baseline information is a goal that needs to be analyzed and solved by students critical thinking. Students develop personal skills and positive attitude towards learning. One of the concepts of active learning is collaborative learning. In today's modern way of teaching, teachers and students are combine intellectual efforts to explore, understand and solve the problem. They generate ideas, and finally create a product. Collaborative learning has a strong influence on critical thinking through discussion, debate and assessment of diferent conclusions. In collaborative learning it is very important to set terms. For example; forming an ideal group of students, selecting members according to mutual interests and viewpoints. At each stage of learning and common interaction the teacher must give students right to opinion. Each member of the group must be responsible for their own work (Individual responsibility, Slavin, 1980). On the other hand group is responsible for each member. Collaborative learning has a role to reduce the feeling of individual loneliness. When a group of students are working together they develop a sense of belonging. The aim of this paper is to indicate the advantages and disadvantages of collaborative learning and specify a need to make this type of learning with maximum results and the development of specific skills. A comparison of collaborative learning with passive learning is in order to prove that the learning in groups give much better results. Students are more independent, happier, less lonely, have sense of belonging and thus enhance learning and encourage personal development.
In the last two decades, we have witnessed the intensive development of artificial intelligence in the field of agriculture. In this period, the transition from the application of simpler machine learning algorithms to the application of deep learning algorithms can be observed. This paper provides a quantitative overview of papers published in the past two decades, thematically related to machine learning, neural networks, and deep learning. Also, a review of the contribution of individual countries was given. The second part of the paper analyses trends in the first half of the current year, with an emphasis on areas of application, selected deep learning methods, input data, crop mentioned in the paper and applied frameworks. Scopus and Web of Science citation databases were used.
<p><span>Temperature and precipitation have a significant </span><span>a</span><span>ffect on the growth of vines and consequently viticulture is highly affected by climate change. Measurements indicate changes in the occurrence of phenological phases in the present climate and it is expected that this trend will continue in future.</span></p><p><span>Istria is one of the most </span><span>prosperous</span><span> wine regions in Croatia with more </span><span>than</span><span> 3000 ha of vineyards. It is well known for cultivating traditional (</span><span>Malvazija istarska</span><span>, Teran, Hrvatica&#8230;) and </span><span>introduced</span><span> varieties (Chardonnay, Merlot&#8230;). Because of that, it is important to determine sign and the robustness of future changes in phenological stages as well as possibility of spring frost occurrence.</span></p><p><span>For this study climate change effect on 21 grapevine varieties cultivated in Institute of Agriculture and Tourism in Pore</span><span>&#269;, Croatia </span><span>were analyzed. Linear correlation between meteorological parameters and phenological stages of each variety and Growing degree day (GDD) thresholds required to begin a particular pheno</span><span>logical stage</span><span> is calculated and tested in present climate and used as a constant in future period.</span></p><p><span>For future changes, daily output of temperature (Tmin, Tmax and Tmean) and total daily precipitation from three CORDEX Regional Climate Models (RCMs) simulations (CLMcom-CCLM4-8-17, SMHI-RCA4, CNRM-ALADIN5.3) for Croatian domain, were used to determine changes in beginning of </span><span>three</span><span> key phenological stages (budburst, flowering and </span><span>veraison</span><span>) in 30-year periods (2011-2040, 2041-2070) compared to historical run (1971-2000). </span></p><p><span>Results show that, as the temperature rises phenological stages occur earlier (especially budburst and harvest). This affects all varieties equally. Earlier budburst leads to possibility of spring frost damage particularly in the inland of Istrian peninsula. Because of that, occurrence of frost was studied in present and in future climate. Day with frost is determined with the use of minimum daily temperature and dew point temperature (Td) calculated from Tmin and relative humidity </span><span>at</span><span> 7CET. Conditions for frost occurrence are suitable if Tmin < 3&#176;C and Td < 0&#176;C. Results show that frost will be one of the </span><span>determinal</span><span> factors </span><span>for grapevine</span><span> in future, as it is in present. </span></p><p>&#160;</p>
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