Orientador(es)
Resumo(s)
In a scenario of worldwide honey bee decline, assessing colony strength is becoming increasingly important for
sustainable beekeeping. Temporal counts of number of comb cells with brood and food reserves offers researchers
data for multiple applications, such as modelling colony dynamics, and beekeepers information on
colony strength, an indicator of colony health and honey yield. Counting cells manually in comb images is labour
intensive, tedious, and prone to error. Herein, we developed a free software, named DeepBee©, capable of automatically
detecting cells in comb images and classifying their contents into seven classes. By distinguishing
cells occupied by eggs, larvae, capped brood, pollen, nectar, honey, and other, DeepBee© allows an unprecedented
level of accuracy in cell classification. Using Circle Hough Transform and the semantic segmentation
technique, we obtained a cell detection rate of 98.7%, which is 16.2% higher than the best result found in
the literature. For classification of comb cells, we trained and evaluated thirteen different convolutional neural
network (CNN) architectures, including: DenseNet (121, 169 and 201); InceptionResNetV2; InceptionV3;
MobileNet; MobileNetV2; NasNet; NasNetMobile; ResNet50; VGG (16 and 19) and Xception. MobileNet revealed
to be the best compromise between training cost, with ~9 s for processing all cells in a comb image, and
accuracy, with an F1-Score of 94.3%. We show the technical details to build a complete pipeline for classifying
and counting comb cells and we made the CNN models, source code, and datasets publicly available. With this
effort, we hope to have expanded the frontier of apicultural precision analysis by providing a tool with high
performance and source codes to foster improvement by third parties (https://github.com/AvsThiago/DeepBeesource).
Descrição
Palavras-chave
Apis mellifera L. Cell classification Deep learning DeepBee software Machine learning Semantic segmentation
Contexto Educativo
Citação
Alves, Thiago S.; Pinto, M. Alice; Ventura, Paulo; Neves, Cátia J.; Biron, David G.; Junior, Arnaldo C.; De Paula Filho, Pedro L.; Rodrigues, Pedro J. (2020). Automatic detection and classification of honey bee comb cells using deep learning. Computers and Electronics in Agriculture. ISSN 0168-1699. 170, p. 1-14