Multispectral Image Processing and Pattern Recognition Techniques for Quality Inspection of Apple Fruits

Machine vision applies computer vision to industry and manufacturing in order to control or analyze a process or activity. Typical application of machine vision is the inspection of produced goods like electronic devices, automobiles, food and pharmaceuticals. Machine vision systems form their judgement based on specially designed image processing softwares. Therefore, image processing is very crucial for their accuracy. Food industry is among the industries that largely use image processing for inspection of produce. Fruits and vegetables have extremely varying physical appearance. Numerous defect types present for apples as well as high natural variability of their skin color brings apple fruits into the center of our interest. Traditional inspection of apple fruits is performed by human experts. But, automation of this process is necessary to reduce error, variation, fatigue and cost due to human experts as well as to increase speed. Apple quality depends on type and size of defects as well as skin color and fruit size. Inspection of apples relative to skin color and fruit size is already automated by machine vision, whereas a robust and accurate automatic system inspecting apples with respect to defects is still in research phase because of highly varying defect types and skin color as well as stem/calyx areas that have similar spectral characteristics with some defects. Stem and calyx areas are natural parts of apple fruit that are highly confused with defects in machine vision systems. Therefore, an automatic inspection system should accurately discriminate between these areas and defected skin, for which researchers have introduced several methods using statistical pattern recognition or artificial neural networks as a classifier. Although artificial neural networks are very efficient tools, comparison of other classifiers for recognition of stem/calyx areas will be enlightening. Hence, performances of several statistical and syntactical classifiers as well as a resampling-based method are compared in our innovative work. Results indicate that support vector machines, a statistical classifier, outperform other methods in recognizing stems and calyxes. Quality category of apples depends partly on size of defect, which requires accurate segmentation. Approaches proposed in the literature to segment external defects of apple fruit are mostly based on global thresholding or Bayesian classification. Thus, defect segmentation by other methods and their comparison will be informative. We introduce an original work comparing defect segmentation performances of numerous methods based on global and local thresholding as well as statistical, syntactical and artificial neural network classifiers. Results reveal that supervised classifiers are best methods in terms of performance. Furthermore, multi-layer perceptrons are found to be the most suitable method for defect segmentation in terms of accuracy and processing speed. An inspection system should take decisions at fruit level and place apples into correct categories. Therefore, once external defects are accurately segmented by minimal confusion with stem/calyx areas using the above two novel techniques, we can perform fruit grading and assign an apple to its corresponding quality category. We introduce a two-category and a novel multi-category grading work using statistical and syntactical classifiers for this purpose. The former is performed because it is coherent with most literature, while the latter provides a more realistic inspection decision. Results of both works reveal that we can reach to high, but not perfect, recognition rates with statistical classifiers and appropriate feature selection.