Abstract
As a product of the deep integration between information technology and modern agriculture, digital agriculture is reshaping traditional agricultural production systems and emerging as a key driver for high-quality development and green transformation in agriculture. This paper systematically reviews the current progress of core technologies and future trends in digital agriculture, with a focus on five representative frontier research areas: (1) multi-source data fusion and intelligent agricultural information sensing technologies; (2) crop yield prediction models and decision optimization methods based on machine learning; (3) the construction and application framework of digital twin agricultural systems; (4) intelligent agricultural machinery and autonomous operation path planning technologies; and (5) agricultural carbon emission monitoring and the development of green production evaluation systems. Supported by interdisciplinary integration of remote sensing, the Internet of Things, big data, and artificial intelligence, these technologies enable refined management and dynamic optimization throughout the agricultural production process, significantly improving resource use efficiency, crop productivity, and product quality. Moreover, digital agriculture demonstrates considerable potential in addressing climate change challenges, ensuring food security, and promoting low-carbon and sustainable agricultural development. Based on an analysis of current technological achievements, this paper further discusses key scientific issues and technical bottlenecks, and proposes future research priorities and development pathways.
