A Deep Learning Approach to classify the Honeybee Species and health Identification

Abstract

Honey bee is one of the charming insect that utilizes a collective behavioral nature to achieve the powerful action. Protecting honey bees is one of the important jobs of every human in the world to preserve the ecological balance. Tracking and determining the several species of the bees over their life span electronically is a tedious work. Automated classification of species is important to preserve the various species of honey bees from danger. The diseases that affect the honey bees during their life span have to be detected autonomously and the spread of the diseases to other healthy honey bees has to be preserved. The proposed technique aims in classifying the several species of honey bees and identifying the diseases that are prone to honey bees. Convolution neural network with two dimensional layers are used as a classifier in the proposed model. Data augmentation using Synthetic Minority Over-sampling Technique (SMOTE) is utilized. More than 5000 images of honey bees with lot of features are used for learning purpose. The proposed methodology attained an accuracy of 86% for subspecies classification and 84% for bee health identification.