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Research on Crop Pest and Disease Detection Technology Based on Unmanned Aerial Vehicles

Author(s)

Baoyi Liao

Affiliation(s)

Ocean College, Shanwei Vocational and Technical College, Shanwei, Guangdong, China

Abstract

To enhance the automation and precision of crop pest and disease monitoring, this study systematically investigates a detection technology framework based on unmanned aerial vehicle (UAV) platforms. The research comprehensively utilizes multispectral and hyperspectral sensors for field data acquisition, constructs an analytical framework integrating traditional machine learning and deep learning models, and quantitatively evaluates algorithm performance through field experiments. The results indicate that deep learning models significantly outperform traditional methods in recognition accuracy, particularly excelling in the detection of typical diseases. However, the study also reveals limitations of these models when facing early-stage symptoms, complex backgrounds, and generalization across different crop types. The conclusion states that this technology system provides an effective tool for precision agriculture. Future work requires continuous optimization in areas such as algorithm lightweighting, edge deployment, and multi-source data fusion to promote its large-scale application in actual production.

Keywords

Unmanned Aerial Vehicle (UAV); Crop Pest and Disease Detection; Deep Learning; Multispectral Sensor; Performance Evaluation

References

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