Exploring the Landscapes of Artificial Intelligence in Agriculture via Content Analysis of YouTube Videos

In the realm of digital content, YouTube hosts an extensive array of agricultural videos highlighting the marvels of artificial intelligence (AI). This study offers a comprehensive examination of English-language YouTube videos focusing on AI in agriculture, employing a rigorous checklist for both qualitative and quantitative analysis. By sampling 180 videos via systematic random sampling, the study ensures the inclusion of recurrent themes. Additionally, 2105 comments were extracted using systematic random sampling and categorized into eight broad categories. Various aspects were examined, including speaker count, subscriber count, total videos per channel, duration, enterprise type, crops grown, program location, shot type, AI techniques, channel theme, content type, and themes of comments, along with their associated sentiments. Intra-coder reliability measures were implemented to ensure the accuracy of the study. The findings were presented in frequency distribution tables derived from mean splits of respective categories. Furthermore, sentiment analysis was applied to the extracted comments, measuring positive, negative, and neutral sentiments across a variety of comments. This analysis underscores not only the profound influence of YouTube in shaping perspectives and disseminating information regarding AI-driven farming methods but also establishes it as a valuable repository, empowering and inspiring individuals in their pursuit of sustainable agricultural practices.