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Generative artificial intelligence is emerging as a potentially important component of precision agriculture through its capacity to create synthetic data, generate agricultural imagery, retrieve and reformulate knowledge, support conversational advisory interfaces, and assist decision-support workflows. However, evidence of technical feasibility is developing faster than evidence of agronomic validity, reliability, farmer-centred usefulness, and responsible deployment. This critical review evaluates generative AI as a distinct agricultural technology class, with particular attention to generative adversarial networks, diffusion models, variational autoencoders, large language models, vision-language models, foundation models, fine-tuning, prompting, domain adaptation, and retrieval-augmented generation. It critically examines the proposed use of synthetic crop, disease, weed, pest, and remote-sensing data for augmentation, class-imbalance mitigation, and label-scarcity support, while emphasizing image realism, biological plausibility, source-data quality, and synthetic-real domain gaps. The review also assesses generative decision support and advisory systems, distinguishing accessible language generation and knowledge retrieval from agronomically validated recommendations. Reliability concerns include hallucination, factual inaccuracy, uncertainty underreporting, poor local adaptation, robustness limitations, benchmark insufficiency, and failures of human oversight. Responsible deployment further requires data governance, privacy protection, accountability, transparency, explainability, farmer trust, digital inclusion, risk monitoring, and assessment of computational and sustainability costs. The review contributes an integrated opportunity-risk interpretation and identifies priorities for domain-specific benchmarks, synthetic-data validation, hallucination measurement, uncertainty-aware assistance, human-in-the-loop evaluation, farmer-centred design, governance documentation, and cautious field-readiness assessment.