Artificial Intelligence Powered End-to-End Agricultural Machinery: Intelligent Transformation of Design and Maintenance Systems

Abstract

Reliable condition monitoring and fault diagnosis in agricultural machinery depend on interpreting sensor measurements under variable field conditions. Noise, occlusion, changing loads, and sensor degradation can obscure relevant features, while complementary measurements offer opportunities to reduce information gaps. This scoping review synthesizes 147 included evidence sources published from 2014 to early 2026, supplemented by 11 contextual publications. It examines sensing, feature extraction, and information fusion across mechanical modeling and design, perception, automation and control, and operation and maintenance. Modeling and design provide physical context for interpreting measurements, while perception and control connect state estimation with operational feedback. Applications in blockage detection and multi-component monitoring illustrate how complementary signals can support abnormal-state identification and operational intervention. However, improvements in perception accuracy or control performance do not independently establish improved fault diagnosis. Reported results often depend on local datasets or controlled platforms, and calibration errors, temporal misalignment, and changing operating conditions can compromise fused estimates. Evidence for reliable early warning, precise fault localization, and diagnostic robustness under degraded sensing remains limited. Future evaluations should compare fused and individual-sensor inputs under matched conditions, including noise, partial sensor failure, and changes across machines and seasons. Linking these comparisons to false alarms, missed faults, warning lead time, and maintenance decisions is essential for establishing the practical contribution of sensor fusion.