Study On Rapid Detection Methods Of Defects On Navel Orange Surface

The presence of surface defects is one of the most influential factors in the price of fruit, since most consumer associates quality with a good appearance and the total absence of external defects. Chinese national standard has a strict rule of number and area of fruit surface defect. Compare with other external quality indexes, such as size, color and shape, fast detecting defect on fruit always is a most difficult, time-consuming and interesting task for researcher. Researchers have done a lot of work for years.Navel oranges are used as samples in this study. Detection methods on eleven types of common surface defects (i.e., thrips scarring, canker spot, dehiscent fruit, anthracnose, copper burn, phytotoxicity, wind scarring, insect damage, scale infestation, heterochromatic stripe and rottenness) and identification methods on two very seriously diseases (canker spot and rottenness) were detailedly studied by using RGB imaging, Vis-NIR hyperspectral imaging and fluorescence imaging. Some problems in detecting navel orange surface defects detection were solved in this study. The developed detection algorithm offered help for developing an automatic, fast and on-line navel orange surface defect detection system. Main contents and results were listed as follows:(1) A method for removing fruit image background by using mask template was introduced. Binary mask template was firstly built. Then, the mask was used to perform background segmentation of static and on-line fruit images, respectively. The results showed that background segmentation rate was 100%, and the information on fruit surface can be kept perfectly. This laid the foundation for further effectively segmenting navel orange surface defects.(2) A novel illumination-reflectance model for correcting non-uniform light on navel oranges and segmentation algorithm based on single-threshold value for extracting defects was proposed. Using this light transform model, the intensities of normal areas on navel orange surface were changed into high values. However, defective areas were still kept low intensities. This light transform overcame low defects segmentation accuracy that was caused by non-uniform light distribution on spherical fruit surface. And, it was also useful for fast segmenting navel orange surface defects based on single-threshold value method. The study results showed that light transform model could correct the whole non-uniform light distribution on navel orange surface compared with edge light compensation algorithm, and its speed in processing an image was more than 30 times comparing with B spline curve fitting method. In order to evaluate performance of this algorithm,6345 regions of interest from 11 types of samples were firstly marked. Then, these marked regions were segmented using the developed algorithm. The results showed that 93.8% segmentation accuracy was achieved.(3) An algorithm to differentiate stem-end from different types of defects was proposed based on calculating intensities of different types of defects from R, G and B component images. The study results showed that 100% stem-end identification rate was obtained using proposed algorithm and developed a big area and elongated region removal algorithm (BER). The stem-end identification method is potential to be used in an on-line neval orange defect sorter since it only includes two subtraction operations, one multiplication operation and one division operation avoiding complex pattern recognition methods.(4) A combination algorithm for deftecting defects on neval orange surface was proposed in this study. The algorithm includes four modules such as background segmentation, light non-uniform transform, stem-end identification and navel identification. The results from 1320 sample images includes 11 types of defects showed 99.1% of fruit with defects and 98.3% of normal fruit were correctly identified. In addition, different requirements of users can be met by adjusting threshold values.(5) Study found that six characteristic wavelengths (630,691,769,786,810 and 875nm) and, alternatively, three wavelengths (691,769 and 875nm) in the visible and near-infrared spectral range could be potentially implemented in multispectral imaging systems for detecting orange peel defects. The defect detection algorithm combining two-band ratio with PCA achieved 93.7% identification rate for orange surface defects and no false positives. It should also be pointed out that simple two-band ratio (R875 691) algorithm could be more effective to identify stem-ends from skin defects compared to pattern recognition algorithms, which increased the computational complexity.(6) Fluorescence hyperspectral imaging system was developed to detect early rottenness on oranges. The optimal band (498.6nm and 591.4nm) for identifying rottenness defects was obtained by using Optimum Index Factor (OIF) method which can overcome some disadvantages caused by large data quantity and strong relativity between adjacent bands from hyperspectral images. Based on ratio images and a segmentation algorithm with double thresholds,100% fruit with rotten area was identified. In addition, the algorithm with double thresholds could also effectively avoid the influence from stem-end fluorescence effect. Therefore, the cost of system and algorithm was decreased.(7) Vis-NIR hyperspectral reflectance imaging system was developed to identify oranges with canker spots from those with other types of peel. Seven important wavelengths (630,687, 765,788,815,833 and 883nm) were obtained in this study. The third principal component image based on obtained seven bands and ratio image Q687/630 based on obtained two bands were used to develop multispectral canker identification algorithm.275 independent samples with 11 types of peel defects were used to estimate the feasibility of algorithm. An overall 98.2% canker identification rate was obtained. In addition, in this study, it should be also noticed that two-band ratio images give better recognition results (97.8%) of discriminating canker from normal and other diseased skin conditions aside from anthracnose and copper burn.The above work provided an important foundation for developing on-line and fast detection equipment of navel orange surface defects using machine vision technology in China.