In the apidologie research area, there is one task that obliges the researcher to classify and count the contents of each comb cell in each frame. With this task is possible to control the progression of brood, bees, and food reserves. Since each frame can have thousands of cells, in most cases this task is done by a human in an approximate way, making it error-prone. The automation of this process, using image analysis represents an evolution in this field.
The honey bee is the world’s most important pollinator of food crops. Almost one-third of the food that we consume each day relies on pollination done mainly by bees. So the creation of software that helps the preservation of this species has a direct impact on our lives.
I am going to show you a few challenges we have faced, from creating comb cell detectors using OpenCV and Shapely, to developing models based on Deep Learning to classify the cell's content using the Caffe framework. With these models, we have obtained accuracy above 98% within eight different classes and solved the proposed problem.
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