Research on the optimization of smart agriculture based on machine learning: Taking Qingdao as an example

Faced with the challenges of rural labor shortage, aging population, and food security pressure, machine learning supported intelligent agriculture has become a promising approach to promote sustainable development in rural areas. However, existing research often overlooks the locality of technology adoption and its impact on local economic development. This study takes the coastal city of Qingdao in China as a typical case, examining the constraints and optimization strategies of machine learning intelligent agriculture from a local development perspective to address this gap. Through literature review and semi-structured interviews, four core bottlenecks were identified: technological adaptation barriers, insufficient power infrastructure, shortage of human capital, and lack of collaborative mechanisms. This study proposes targeted and situation specific improvement paths that are consistent with local resource endowments and institutional conditions. The research results not only provide practical guidance for the upgrading of smart agriculture in Qingdao, but also contribute to the literature on digital agriculture and local economic development, and provide valuable insights for other coastal areas facing similar challenges.