Ergonomic, Energy, and Performance Evaluation of Self-Propelled Agricultural Machines
Suraj Jadhav, Sagar Kamble, Sachin Patil, Dnyaneshwar Raut, Sudarshan Shende
Journal of Scientific Research and Reports · pp. 790–804 · Published 25 May 2026
10.9734/jsrr/2026/v32i54219Abstract
Self-propelled agricultural machinery such as tractors, self-propelled sprayers, transplanters, and combine harvesters have become central to modern crop production because they compress field operations into narrow seasonal windows while reducing dependence on manual labour. However, the productivity gains of mechanisation are often accompanied by elevated operator exposure to whole-body vibration, noise, awkward or sustained postures, repetitive control actions, visual strain, and fatigue-related safety risks. In parallel, machine performance is frequently reported using technical indicators (e.g., field productivity, work quality, energy use), but these are rarely integrated with operator-centred outcomes in a way that supports design optimisation and technology adoption. This review synthesises recent research on ergonomic evaluation methods and performance evaluation approaches for self-propelled agricultural machinery, highlighting how operator comfort, workload, and health risks interact with operational efficiency and work quality. Evidence from vibration transmissibility studies, machine-learning-based vibration prediction, ergonomics of assisted driving, and experimental evaluations of sprayers, transplanters, and harvesting systems is used to identify converging trends and persistent gaps. The review proposes a structured perspective in which ergonomic risks are treated as measurable constraints that directly affect performance stability, operator decision quality, and system sustainability. Practical implications are outlined for integrated testing protocols, sensor-based monitoring, and design pathways aligned with emerging electrification and automation in agriculture. Emerging automation and electrification will reshape ergonomic baselines and workload distribution, making updated duty-cycle–based testing protocols critical for ensuring that new technologies improve both usability and performance reliability.
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