Advanced technologies for precision tree fruit disease management: A review

Summary

This review synthesises current knowledge on the application of advanced technologies—including unmanned aerial vehicles, ground-based sensors, and machine learning—to tree fruit disease management. The authors evaluate how these precision agriculture tools enhance disease scouting and predictive modelling whilst identifying critical gaps in data quality, computational robustness, and model generalisation that must be addressed to realise their full potential in sustainable horticultural systems.

Regional applicability

The review's findings on precision disease management technologies are broadly applicable to UK tree fruit producers (particularly apples, pears, and soft fruits), though the practical adoption barriers around data infrastructure and computational capacity may be more pronounced for smaller UK holdings. Regional disease pressures and weather patterns differ from some global contexts, necessitating localised model validation before deployment.

Key measures

Effectiveness of precision agriculture tools and machine learning approaches in disease detection, monitoring, and prediction; data quality and computational challenges in implementation

Outcomes reported

This review evaluated the effectiveness of UAVs, UGVs, sensors, machine learning algorithms, and predictive models in disease scouting, monitoring, detection, and prediction for tree fruit cultivation. The paper identified both the transformative potential of these technologies for enhancing crop health and reducing losses, and persistent challenges including data quality, computational demands, and model generalisability.

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