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JAFARI, A., BAKHSHIPOUR, A., HEMMATIAN, R.. (1393). Integration of Color Features and Artificial Neural Networks for In-field Recognition of Saffron Flower. , 33(1), 1-14. doi: 10.22099/iar.2014.2376
A. JAFARI; A. BAKHSHIPOUR; R. HEMMATIAN. "Integration of Color Features and Artificial Neural Networks for In-field Recognition of Saffron Flower". , 33, 1, 1393, 1-14. doi: 10.22099/iar.2014.2376
JAFARI, A., BAKHSHIPOUR, A., HEMMATIAN, R.. (1393). 'Integration of Color Features and Artificial Neural Networks for In-field Recognition of Saffron Flower', , 33(1), pp. 1-14. doi: 10.22099/iar.2014.2376
JAFARI, A., BAKHSHIPOUR, A., HEMMATIAN, R.. Integration of Color Features and Artificial Neural Networks for In-field Recognition of Saffron Flower. , 1393; 33(1): 1-14. doi: 10.22099/iar.2014.2376

Integration of Color Features and Artificial Neural Networks for In-field Recognition of Saffron Flower

Iran Agricultural Research
مقاله 1، دوره 33، شماره 1، آذر 2014، صفحه 1-14    اصل مقاله (501.64 K)
نوع مقاله: Full Article
شناسه دیجیتال (DOI): 10.22099/iar.2014.2376
نویسندگان
A. JAFARI* 1؛ A. BAKHSHIPOUR2؛ R. HEMMATIAN2
1Shiraz University,
2Shiraz University
چکیده
ABSTRACT-Manual harvesting of saffron as a laborious and exhausting job; it not only raises production costs, but also reduces the quality due to contaminations. Saffron quality could be enhanced if automated harvesting is substituted. As the main step towards designing a saffron harvester robot, an appropriate algorithm was developed in this study based on image processing techniques to recognize and locate saffron flowers in the field. Color features of the images in HSI and YCrCb color spaces were used to detect the flowers. High pass filters were used to eliminate noise from the segmented images. Partial occlusion of flowers was modified using erosion and dilation operations. Separated flowers were then labeled. The proposed flower harvester was to pick flowers using a vacuum snapper. Therefore, the center of the flower area was calculated by the algorithm as the location of the plant to be detected by the harvesting machine. Correct flower detection of the algorithm was measured using natural images comprising saffron, green leaves, weeds and background soil. The recognition algorithm’s accuracy to locate saffron flowers was 96.4% and 98.7% when HSI and YCrCb color spaces were used. Final decision making subroutines utilize artificial neural networks (ANNs) to increase the recognition accuracy. A correct detection rate of 100% was achieved when the ANN approach was employed.
کلیدواژه‌ها
Keywords: Artificial neural networks؛ Saffron؛ Machine vision؛ Harvester
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