Digital agriculture is becoming more and more in demand as a result of the quick advancement of information technology. Crop yield has always garnered a lot of interest as a significant problem in agricultural production. At the moment, machine learning and artificial intelligence in general are the most popular methods for predicting agricultural productivity. Consequently, one of the main problems in digital agriculture is creating a machine learning technique that can reliably forecast crop yield. Crop yield prediction has a strong time correlation, in contrast to conventional regression prediction problems. For instance, there are significant temporal correlations in the weather data for every county. Furthermore, crop yield is somewhat impacted by geographic data from various places. For instance, a county is likely to have large yields if its neighbouring countries have a strong harvest. We used models like random forest, decision tree classifier, support vector machine, KNN, and logic regression in this study. With an accuracy score of 99.77%, random forest produced the greatest results out of all of them.

In our research study spanning 2015-2021, we analyzed Software Engineering (SE) across 15 Knowledge Areas (KAs) outlined by SWEBoK. Using Web of Science (WoS) database, we examined 32,232 articles (2015-2017), 31,459 articles (2018-2020), and 6,919 articles (2021). Collaborative and co-word networks were generated using VOS Viewer. Our findings reveal SE's influence in 81 research areas. English dominated, but Spanish and Portuguese were notable. Europe led in publications, with USA and China prominent. Stronger collaboration networks, especially in the USA and China, correlated with increased research output. Asian countries' rise was evident from 2018-2021. Top 10 highly cited papers and co-word analysis were included, aiding researchers and educators. SE's impact crossed domains, enabling cross-disciplinary research and informing educators' course designs. Institutional and country productivity influenced partnerships and academia-industry collaborations. Multinational companies gauged research potential through active countries. Analyzing co-words depicted trends, and highly cited papers set industry standards. Bibliometric analysis offered qualitative and quantitative research evaluation.