Evaluation of computational algorithms for digital recognition of alveoli in Africanized bee combs
DOI:
https://doi.org/10.52945/rac.v36i3.1735Keywords:
Apis mellifera, Neural networks, Digital images, DeepBeeAbstract
Analogical methodologies are widely used to assess the general condition, monitor and estimate the development
of Apis mellifera bee colonies. Despite their importance, these methods used during the field stage are complex, timeconsuming and invasive. The free software DeepBee© was used to automatically recognize and classify alveoli in digital images to verify the possibility of eliminating these difficulties. The digital images were obtained from an Android cell phone using a camera with artificial lighting and Bluetooth. The images were taken in two Langstroth hives, totaling 28 digital images. The hives were in the municipalities of Videira and Caçador in SC. The DeepBee© software automatically detected seven classes of alveoli: eggs, larvae, operculated offspring, pollen, nectar, honey and others. Digital processing, mathematical topology and
pattern recognition algorithms using DeepBee© neural networks allowed the identification of the general state of the colonies. Some flaws in pattern recognition suggest the need for new training of the DeepBee© software neural network to make it an operational tool for monitoring the development of Apis mellifera Africanized colonies.
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Copyright (c) 2023 Hamilton Justino Vieira, Carlos Eduardo Salles de Araújo, André Amarildo Sezerino, Tânia Patrícia Schafaschek, Rafael Canan
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