Automatic Detection and Classification of External Olive Fruits Defects

Olives are an important agricultural product, therefore the industry is interested in detecting their external defects. The researchers Nashat Hussain Hassan and Ahmed Nashat from Fayout University, Egypt, have developed an image processing method that can classify healthy or defected olive fruits. Furthermore, a series of techniques have been compared to find the most appropriate low-cost kit that can be used in a real application.


a Healthy olives, b defected class (A), c defected class (B)

The first developed algorithm is called Texture Homogeneity Measuring Techique (T.H.M.T) and it consists of five steps. First, images are collected and then pre-processed by applying a grayscale conversion. The next step is to extract objects by segmenting the images into olives and background. The defects are obtained by scanning the image horizontally and pixels are labeled accordingly with ‘0’ for a healthy area and ‘1’ if a defect is present.

The second proposed method is called Special Image Convolution Algorithm (S.I.C.A.) and it is similar to edge detection, but with specific kernels of 7×7 which are applied both horizontally and vertically. The results are then thresholded based on the values observed by the authors.

Hassan, Nashat M. Hussain, and Ahmed A. Nashat. “New effective techniques for automatic detection and classification of external olive fruits defects based on image processing techniques.”¬†Multidimensional Systems and Signal Processing¬†30.2 (2019): 571-589.

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