Pulse Brain · Growing Health Evidence Index
Peer-reviewed

Deep Learning-Based Weed–Crop Recognition for Smart Agricultural Equipment: A Review

Hao-Ran Qu, Wen‐Hao Su

Agronomy · 2024

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Summary

Weeds and crops engage in a relentless battle for the same resources, leading to potential reductions in crop yields and increased agricultural costs. Traditional methods of weed control, such as heavy herbicide use, come with the drawback of promoting weed resistance and environmental pollution. As the demand for pollution-free and organic agricultural products rises, there is a pressing need for innovative solutions. The emergence of smart agricultural equipment, including intelligent robots, unmanned aerial vehicles and satellite technology, proves to be pivotal in addressing weed-related challenges. The effectiveness of smart agricultural equipment, however, hinges on accurate detection, a task influenced by various factors, like growth stages, environmental conditions and shading. To

Source type
Peer-reviewed study
DOI
10.3390/agronomy14020363
Catalogue ID
SNmok1w0o2-3fhgbz
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