Abstract
Citrus Huanglongbing (HLB) is a destructive disease in citrus production that causes huge economic damage to citrus producers and related industries in the world. Early and accurate detection of HLB is a critical management step to control the spread of this disease. However, existing HLB detection methods cannot be widely adopted in citrus production due to long-time and high-cost detection period in specific laboratory environments. In view of this, a fast-response and low-cost computer vision technique is investigated for diagnosing HLB in citrus leaves. Specifically, the Gaussian mixture density (GMD) is performed to extract the leaf object from the citrus image, followed by the feature extraction and recognition of the existence of HLB in the leaf based on scalable vocabulary tree. A citrus leaf image dataset is constructed, and the experimental results show that the proposed HLB recognition method with GMD object extraction performs 95–100 % accuracy within 1 s.
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Acknowledgments
Supported by National Natural Science Foundation of China (31201129), China Agriculture Research System (CARS-27) and Guangdong Science and Technology Project (2011B-020308009).
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Deng, XL., Li, Z., Deng, XL. et al. Citrus disease recognition based on weighted scalable vocabulary tree. Precision Agric 15, 321–330 (2014). https://doi.org/10.1007/s11119-013-9329-2
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DOI: https://doi.org/10.1007/s11119-013-9329-2