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Identification of Grapefruit Black Spot Based on Hyperspectral Imaging using Naïve-Bayes Classifier

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DOI: 10.23977/hyde.2022.010101 | Downloads: 23 | Views: 372


Sitan Ye 1, Haiyong Weng 2


1 School of Engineering, Newcastle University, Newcastle, UK
2 College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou, China

Corresponding Author

Haiyong Weng


Citrus black spot (CBC) was considered as one of quarantine diseases to citrus production all over the world. Timely removal citrus infected by CBC can stop it propagation and reduce economic losses in the process factory at harvest time. Hyperspectral images were obtained from CBC-infected, healthy and melanose grapefruits, respectively. Principle component analysis (PCA) and random frog (RF) were performed to select optimal features for classification. Two supervised classifiers, Naïve-Bayes and K-nearest neighbour algorithm (KNN), were implemented to compare their classification performance. It found that five wavelengths (488, 532, 534, 682 and 684 nm) selected by PCA combining with Naïve-Bayes (PCA-NB) achieved a best classification accuracy of 100% while PCA-KNN, RF-KNN, and RF-NB reached 86.1%, 86.2% and 86.1%, respectively. The research has demonstrated that hyperspectral imaging was a potential technology for grapefruit black spot on-line detection.


Grapefruit black spot, Hyperspectral imaging, Classification, Random frog, Principle component analysis, Naïve-Bayes


Sitan Ye,  Haiyong Weng, Identification of Grapefruit Black Spot Based on Hyperspectral Imaging using Naïve-Bayes Classifier. Agricultural Mechanization, Electrification and Automation (2022) Vol. 1: 1-9. DOI:


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