Innovative agricultural technologies encompass a wide range of tools and techniques aimed at improving efficiency, sustainability, and productivity in farming. This study analyzes patents related to the use of artificial intelligence (AI) in agriculture. Various patent databases were utilized, employing keywords and terms such as “AI in agriculture”, “deep learning in agriculture”, “machine learning in agriculture”, “AI applications in crop yield optimization”, “crop monitoring with AI”, and “pest and disease detection using AI.” Searches were carried out using patent titles, abstracts, and claims to ensure thorough coverage and the retrieval of pertinent data. The search results were then refined based on publication year, patent classifications, applicants, and jurisdictions. As a result, 1514 patent documents were identified. The origins of AI use in agriculture patenting can be traced back to the earliest priority date, marking 1989 as the inaugural year. Significantly, the peak of patent document activity occurred in 2023. The analysis reveals that the United States and China are the most prolific nations in patenting AI applications in agriculture. The majority of inventions involve information and communication technology tailored for agriculture, fishing, and mining. Additionally, patents in this area are related to computing arrangements based on specific computational models, particularly focusing on machine learning and neural networks inspired by biological models. This study provides a patent analysis and competitive analysis covering AI usage trends in agriculture and presents recommendations to guide the development of innovative research strategies.