Lakhasly

Global agriculture is undergoing a paradigm shift from conventional sustainability toward regenerative systems capable of delivering measurable environmental, economic, and social gains.The synthesis identifies four major research gaps that currently constrain widespread adoption: fragmented digital ecosystems, limited transferability of AI models across agroecological regions, the absence of standardized sustainability benchmarks, and socio-economic barriers affecting technology accessibility.Furthermore, the review evaluates digitally guided pathways for soil carbon sequestration, biodiversity enhancement, water-use efficiency, greenhouse-gas mitigation, and ecosystem-service restoration, while examining governance, digital equity, data ownership, and policy mechanisms necessary for large-scale implementation.While advances in artificial intelligence (AI), the Internet of Things (IoT), digital twins, and precision agriculture have accelerated agricultural digitalization, current research remains fragmented, with limited integration of these technologies into a unified framework for achieving net-positive farming.

Global agriculture is undergoing a paradigm shift from conventional sustainability toward regenerative systems capable of delivering measurable environmental, economic, and social gains. While advances in artificial intelligence (AI), the Internet of Things (IoT), digital twins, and precision agriculture have accelerated agricultural digitalization, current research remains fragmented, with limited integration of these technologies into a unified framework for achieving net-positive farming. This review synthesizes recent advances (2015–2026) to establish net-positive farming as a systems-oriented paradigm in which digital technologies actively support ecosystem regeneration while sustaining or improving agricultural productivity. The review first defines the conceptual foundations, performance metrics, and measurement, reporting, and verification (MRV) frameworks required to quantify net-positive outcomes. It then critically examines precision agriculture, climate-smart agriculture, IoT sensing infrastructures, AI-enabled decision support systems, digital twins, cyber-physical systems, and closed-loop AIoT architectures as complementary components of intelligent regenerative farming. Furthermore, the review evaluates digitally guided pathways for soil carbon sequestration, biodiversity enhancement, water-use efficiency, greenhouse-gas mitigation, and ecosystem-service restoration, while examining governance, digital equity, data ownership, and policy mechanisms necessary for large-scale implementation. Particular emphasis is placed on emerging technologies, including reinforcement learning, explainable AI, edge intelligence, and digital-twin-enabled adaptive management, together with their roles in automated MRV and carbon-market participation. The synthesis identifies four major research gaps that currently constrain widespread adoption: fragmented digital ecosystems, limited transferability of AI models across agroecological regions, the absence of standardized sustainability benchmarks, and socio-economic barriers affecting technology accessibility. Building upon these findings, this review proposes an integrated roadmap that unifies intelligent sensing, predictive analytics, adaptive decision-making, and environmental verification into a scalable framework for regenerative digital agriculture. The proposed perspective positions AI-driven net-positive farming as a foundational strategy for climate resilience, carbon-neutral food production, and the transition toward nature-positive agricultural systems.