Search

Quantifying soil total nitrogen (TN) with high accuracy is vital for boosting crop vigor and quality. A major obstacle, however, is that soil diversity from one region to another undermines the transferability of calibration models. To confront this obstacle, this paper puts forward a framework called transfer-learning-assisted laser-induced breakdown spectroscopy for cross-regional soil analysis (TransLIBS-CRS), which is custom-built to counteract the weak cross-domain predictive capacity caused by differences in regional soil characteristics. By adapting the model with a small batch of samples from the target setting, the technique substantially elevates the cross-domain utility of LIBS measurements, easing the difficulties tied to procuring soil specimens from geographically dispersed locations. In tests where Beijing data were used to infer TN levels in Guangzhou, the TransLIBS-CRS model yielded the strongest outcomes, recording an R2V of 0.846 and an RMSEV of 0.814 g/kg. A closer look via saliency mapping and chemometric tools indicated that carbon emission wavelengths at 193.0 nm and 247.8 nm serve a decisive function in TN quantification. Crucially, these spectral markers kept their predictive power unchanged when carried over to the Guangzhou soil dataset. This tactic furnishes a workable route toward broad-scale, high-efficiency soil TN assessment.

Autonomous farming systems are moving rapidly from experimental prototypes into commercial and operational agricultural settings. Autonomous tractors, robotic weeders, drones, harvest-assist systems, and sensor-guided machines promise to address labour shortages, improve operational efficiency, and support more precise field management. Yet the governance arrangements needed to ensure that these systems remain safe, understandable, and accountable have not developed at the same pace. The central problem is not only whether autonomous machines can perform agricultural tasks accurately. It is whether farmers, workers, contractors, and rural communities can understand, supervise, question, override, and hold these systems accountable when they act in complex farm environments. Without deliberate attention to trust calibration, human override, explainability, and accountability, autonomous farming may produce technically impressive but socially fragile systems. This article develops a Human-Centred Governance Framework for autonomous farming. The framework is designed around four pillars: calibrated trust, meaningful human override, explainable decision-making, and clear accountability structures. Its purpose is to shift governance attention from machine capability alone toward the conditions under which autonomous systems can be responsibly integrated into agricultural work. The proposed framework shows that human-centred governance is not an optional ethical supplement to autonomous farming. It is a practical requirement for safe deployment, legitimate adoption, and accountable operation. By integrating trust, override, explainability, and accountability, the framework provides guidance for farm managers, technology developers, regulators, and agricultural service providers.