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Leaching losses of nitrogen (N) from grazed pasture are driven predominantly by urinary N deposited by cattle. This work aimed to use non-invasive accelerometer-based sensors to capture the timing and duration of urination and defecation events in grazing cattle. To register the back arching motion that accompanies urination and defecation, each cow was fitted with two accelerometer sensors. Experiments took place outdoors under grazing conditions during two seasons—autumn and summer—and captured a total of 160 urination events. Across the majority of these events, the tilt angle was observed to shift within a range of 8 to 22.5 degrees. Using 18 selected features that characterise a cow's back arching, ensemble machine learning classifiers were constructed to recognise urination and defecation. The urination classifier delivered an F1 score of 0.80 (harmonic mean computed from a precision of 0.82 and a sensitivity of 0.78). In contrast, the defecation classifier gave an F1 score of 0.73 (calculated from 408 hours of recordings made between 9 a.m. and 3 p.m. from 19 cows). Predictive models for urination event duration were statistically significant. Still, they underperformed, with low explanatory power, in part because the relationship between the duration of back arching and the duration of urine passage was only moderate (R² values of 0.29 in autumn and 0.47 in summer). The work highlights that non-invasive accelerometer sensing has real promise for capturing individual cows' urination patterns and for generating animal-level information that farm decision support systems (such as those addressing urine-N excretion) and managerial actions (such as selective culling or breeding) could use as input.

Sustainable precision agriculture increasingly depends on the convergence of artificial intelligence, robotics, automation, and Internet of Things technologies rather than on isolated digital tools. This integrative review synthesizes how these technology domains interact across sensing, data transmission, analytics, decision support, robotic or automated actuation, and feedback within sustainability-oriented farming systems. The review uses a curated peer-reviewed literature selection and an integrative evidence-charting logic to map technology domains, agricultural applications, integration mechanisms, decision functions, sustainability relevance, and system-level challenges. The AI focus includes machine learning, deep learning, computer vision, predictive analytics, data fusion, and decision-support models used for crop monitoring, yield estimation, input optimization, pest and disease detection, and risk forecasting. The robotics and automation focus covers autonomous field robots, UAVs, smart machinery, task execution, and automated farm operations. The IoT and sensing focus includes wireless sensor networks, distributed monitoring, machinery telemetry, remote sensing, cloud and edge computing, and data platforms. The synthesis shows that technology convergence may support resource-efficient, climate-sensitive, and more responsive farm management, but only when data quality, interoperability, field robustness, explainability, affordability, governance, usability, and farmer or advisor feedback are addressed. The review contributes a system-level framing of sustainable precision agriculture as a socio-technical feedback system in which digital sensing, AI interpretation, automated action, and human decision-making must be jointly designed. Future research should prioritize validated, interoperable, transparent, and farmer-centered integration pathways rather than assuming that more automation or more data automatically improves sustainability outcomes.