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The citrus industry has need for effective and efficient approaches to remove fruit with canker before they are shipped to selective international markets. The objective of this research was to study the effect of fruit harvest time on citrus canker detection using hyperspectral reflectance imaging. Ruby Red grapefruits with normal surface, canker, and five common peel diseases including greasy spot, insect damage, melanose, scab, and wind scar were collected during a 7-month harvest period. Hyperspectral reflectance images were acquired in the wavelength range of 450-930 nm. Spectral information divergence (SID) was used as a discrimination measure to perform statistical comparisons among reflectance spectra of grapefruit samples over the whole harvest season. The SID values with respect to the mean reflectance spectrum of canker for the 7-month periods were 0.0009, 0.0002, 0.0008, 0.0001, 0.0007, 0.0003, and 0.0004, respectively. Correlation analysis (CA) was used for hyperspectral band selection. Two-band ratio images using wavelengths of 729 and 834 nm selected by CA (R834/R729) gave the maximum absolute correlation value of 0.811. The mean ratio values for canker were in the range from 1.287 to 1.407, which were higher than the ratio values for other peel conditions. A simple thresholding and morphological filtering operations were applied to the two-band ratio images. The classification accuracies were in the range of 93.3-96.7% for each month. The results presented in this study demonstrated that there is no significant difference among the accuracy for canker detection over the whole harvest season using the two-band ratio images and threshold based on the spectrum of 7-month average.

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The Neighbourhood-based collaborative filtering (CF) algorithm has been widely used in recommender systems. To enhance the adaptability to the sparse data, a CF with new similarity measure and prediction method is proposed. The new similarity measure is designed based on the Hellinger distance of item labels, which overcomes the problem of depending on common-rated items (co-rated items). In the proposed prediction method, we present a new strategy to solve the problem that the neighbour users do not rate the target item, that is, the most similar item rated by the neighbour user is used to replace the target item. The proposed prediction method can significantly improve the utilisation of neighbours and obviously increase the accuracy of prediction. The experimental results on two benchmark datasets both confirm that the proposed algorithm can effectively alleviate the sparse data problem and improve the recommendation results.

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