Summary
This review synthesises the application of deep learning for automated weed–crop recognition in smart agricultural equipment. The authors examine how advances in sensors, algorithms and deep learning models enhance detection accuracy under variable field conditions, enabling precision weed management that reduces herbicide use and production costs. The paper addresses current technical challenges and proposes prospects for integrating intelligent equipment into sustainable, efficient weed control strategies.
Regional applicability
The technical approaches reviewed are geography-agnostic and applicable to UK arable production systems where herbicide resistance and environmental concerns drive adoption of precision agriculture. However, implementation would depend on UK availability of compatible hardware and local validation of algorithm performance under British growing conditions and weather patterns.
Key measures
Weed recognition accuracy; detection performance across growth stages, environmental conditions and shading; algorithm effectiveness for precision plant identification
Outcomes reported
The paper reviews the application of deep learning techniques for distinguishing weeds from crops using smart agricultural equipment, including intelligent robots, UAVs and satellite technology. It reports on how optimised algorithms and suitable sensors enable targeted weed management actions such as minimal pesticide spraying or laser excision, reducing production costs.
Topic tags
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