## 2 Review Methodology  
To ensure this survey's systematic and comprehensive nature, we designed and executed a rigorous literature search and screening process following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [50], as illustrated in Figure 2. The literature for this study was sourced from the following major academic databases: IEEE Xplore, ACM Digital Library, Elsevier ScienceDirect, SpringerLink, and Google Scholar. To comprehensively cover the field's foundational and recent advancements, we set the publication time frame from 2009 to 2025, with a strong focus on the latest literature from the past five years, while foundational earlier works were included when historically significant.  
Our search strategy employed a structured combination of keywords, which were divided into four core dimensions to ensure broad and deep coverage of the research area.  
* **Core Themes:** This dimension defined the study's broader context, including terms such as \"Precision Agriculture\", \"Smart Farming\", and \"Digital Agriculture\".
* **Core Technologies:** This dimension focused on the key technical methods central to this review, with search terms like \"Artificial Intelligence (AI)\", \"Computer Vision\", \"Deep Learning\", \"Machine Learning\", \"Generative AI\", \"Generative Adversarial Networks (GANs)\", \"Foundation Models\", \"Convolutional Neural Networks (CNNs)\", and \"Transformer\".  
Figure 1: Overview of computer vision and generative AI methods in smart agriculture applications.  
* **Application Tasks:** This dimension aimed to cover specific application scenarios of AI and vision in agriculture, including keywords such as \"Disease Detection\", \"Pest Detection\", \"Weed Detection\", \"Yield Prediction\", \"Fruit Grading\", \"Maturity Detection\", \"Behaviour Recognition\", \"Robotic Harvesting\", and \"Autonomous Navigation\".
* **Enabling Systems and Sensing Technologies**: This dimension covered the hardware platforms and data acquisition technologies supporting the aforementioned applications, for instance, \"Unmanned Aerial Vehicle (UAV)/Drone\", \"Internet of Things (IoT)\", \"Remote Sensing\", \"Hyperspectral Imaging\", \"Multispectral Imaging\", \"Stereo Vision\", and \"Edge Computing\".  
During the actual search, we utilised Boolean operators (AND, OR) to flexibly combine keywords from different dimensions to construct precise search queries. A typical query example is as follows: (\"Computer Vision\" OR \"Deep Learning\") AND (\"Precision Agriculture\" OR \"Smart Farming\") AND (\"Disease Detection\" OR \"Weed Detection\" OR \"Fruit Grading\").  
The literature screening process was divided into two stages. In the initial screening, we reviewed titles and abstracts to exclude entries clearly irrelevant to the survey's topic; simultaneously, non-English literature, short abstracts, and articles without full-text access were eliminated. In the secondary screening, we conducted a full-text review of the remaining literature, making the final selection based on the following inclusion criteria: (1) the research content is highly relevant to the application of computer vision and  
Figure 2: PRISMA flow diagram.  
artificial intelligence in agriculture; (2) the study includes a clear methodology, detailed experimental procedures, and verifiable results. Following this screening process, a total of 309 publications were included for analysis in this survey.