Development of CNN architecture for Honey Bees Disease Condition

Honey bees are one of the most important pollinators for a wide range of products in the food chain. Today, with the help of developing technology, observing bees healthy controls is a very important field of study. In this study, the images taken in the natural environment of the bees were processed with Convolutional Neural Network (CNN) architecture and the health status of the bees were classified. The results obtained were promising for the studies to be carried out in this area. In addition, the structure of the CNN architectures was studied and the CNN architectures with different types and number of layers were compared with each other. As a result of the comparison, using the ideal number of convolution layers instead of using a great number of convolution layers for CNN architectures, increases the success. In addition, the use of normalization layers that serve as supporters in CNN architectures has been found to be very important for increasing success. In this study, 5 different CNN architectures were developed and the classification results obtained with these architectures were analyzed. Among the architectures developed, KM_1 network architecture has achieved the best results with a success rate of 92,42.