Document Type : Research Paper
Authors
1 Faculty Member, Department of Agricultural Engineering, Faculty of Shahriar, Technical and Vocational University
2 Specialist, Department of Agricultural Engineering, Faculty of Shahriar, Technical and Vocational University
Abstract
This study was performed to intelligent and rapid assessment of the status of colonies in terms of honey production efficiency during foraging period, and presenting a method based on machine vision system. Using deep learning method, at first the comb frame and then the geometric, textural and color pattern of honey were identified. After that, the percentage of honey area was calculated. To do this, the imaging test of bee colonies using digital camera was designed and performed in such a way that different states of cells were present on the combs. In image analysis stage, the convolutional neural network with YOLOv5 algorithm and semantic segmentation method were used. The results showed that the present intelligent system has the ability to detect the comb frame from the surrounding environment of the image with an accuracy of more than 88%. Also, honey-related areas in each comb were identified with almost 83% accuracy and about 240 times quicker that of an expert beekeeper. These results were simultaneously confirmed with manual counting by a skilled beekeeper. Due to increase in the estimation speed, reduction of human error and consequently reduction of disruption time in colony activity, the proposed method can be a proper alternative to the traditional method of using framing technique for regular visits and evaluation of honey production efficiency.
Keywords
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