PrivTab-GAN: A privacy-preserving generative adversarial network for synthetic tabular agricultural data

The increasing demand for data-driven agricultural decision making is hindered by challenges such as data shortages, privacy difficulties, and the need for robust generalization across diverse farming situations. This paper introduces PrivTab-GAN, a privacy-preserving generative adversarial network developed for generating synthetic tabular agricultural data. Evaluation on three practical datasets, Crop Recommendation, Weather Prediction, and Water Irrigation, indicates that PrivTab-GAN surpasses prior models (conditional tabular-GAN, information-theoretic GAN, conditional tabular-GAN, and conditional Wasserstein-GAN), achieving K–S test values that are 6%–12% lower and Jaccard index scores that are 8%–15% higher. The proposed framework, which utilizes a domain-adversarial neural network, demonstrates remarkable generalization performance, attaining 94%–97% accuracy with a maximum reduction of 7.5% even under rigorous privacy constraints (ε = 0.5, σ = 2.0) across two evaluation perspectives (original-to-synthetic and synthetic-to-original training/testing). Moreover, PrivTab-GAN maintains 97.4% of the original data usefulness; however, trade-offs arise with heightened gradient clipping (C = 1.5), leading to a performance decline of up to 18%. These findings validate PrivTab-GAN as a scalable, privacy-preserving methodology for synthetic data-driven agricultural AI, enabling applications including crop planning, irrigation optimization, and climate-adaptive farming.