Detection of bruises on apples using near-infrared hyperspectral imaging

Development of an automated bruise detection system will help the fruit industry to provide better fruit for the consumer and reduce potential economic losses. The objective of this research was to investigate the potential of near-infrared (NIR) hyperspectral imaging for detecting bruises on apples in the spectral region between 900 nm and 1700 nm. An NIR hyperspectral imaging system was developed and a computer algorithm was created to detect both new and old bruises on apples. Experiments were conducted to acquire hyperspectral images from Red Delicious and Golden Delicious apples over a period of 47 days after bruising. Results showed that the spectral region between 1000 nm and 1340 nm was most appropriate for bruise detection. Bruise features changed over time from lower reflectance to higher reflectance, and the rate of the change varied with fruit and variety. Using both principal component and minimum noise fraction transforms, the system was able to detect both new and old bruises, with a correct detection rate from 62% to 88% for Red Delicious and from 59% to 94% for Golden Delicious. The optimal spectral resolution for bruise detection was between 8.6 nm and 17.3 nm, with the corresponding number of spectral bands between 40 and 20. This research shows that NIR hyperspectral imaging is useful for detecting apple bruises. With improvement in image acquisition speed and detector technology, the NIR hyperspectral imaging technique will have the potential for offline inspection and online sorting of fruit for defects.