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M. Pravin, G. Ayappan, M. Mailsamy


Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. A solution for the detection and classification of fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following steps; in the first step K-Means clustering technique is used for the image segmentation, in the second step some features are extracted from the segmented image, and finally images are classified into one of the classes by using a Support Vector Machine. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of fruit diseases. Fruits should be quickly and correctly differentiated from their surroundings for the fruit harvesting robot. Edge based and color based detection methods are generally used to segment images of fruits obtained under natural lighting conditions. In this work, Digitized images of mango fruits along with its background were selected from the Internet in order to find a mango in each image and to locate its exact position. We compared the results of Edge based and colored based segmentation results and found that color based segmentation outperforms the edge based segmentation in all aspects. The comparison results are shown in the segmented image results. Accordingly, a new mango detection method is proposed to position the centroid of mangoes


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M. Pravin, G. Ayappan, M. Mailsamy. (2022). SMART FRUIT QUALITY DETECTION USING IOT AND IMAGE PROCESSING TECHNIQUE. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 11(3), 1565–1569.
Research Articles