Detection and Assessment of White Flowering Nectar Source Trees and Location of Bee Colonies in Rural and Suburban Environments Using Deep Learning

Abstract

Environmental pollution with pesticides as a result of intensive agriculture harms the development of bee colonies. Bees are one of the most important pollinating insects on our planet. One of the ways to protect them is to relocate and build apiaries in populated areas. An important condition for the development of bee colonies is the rich species diversity of flowering plants and the size of the areas occupied by them. In this study, a methodology for detecting and distinguishing white flowering nectar source trees and counting bee colonies is developed and demonstrated, applicable in populated environments. It is based on UAV-obtained RGB imagery and two convolutional neural networks—a pixel-based one for identification of flowering areas and an object-based one for beehive identification, which achieved accuracies of 93.4% and 95.2%, respectively. Based on an experimental study near the village of Yuper (Bulgaria), the productive potential of black locust (Robinia pseudoacacia) areas in rural and suburban environments was determined. The obtained results showed that the identified blooming area corresponds to 3.654 m2, out of 89.725 m2 that were scanned with the drone, and the number of identified beehives was 149. The proposed methodology will facilitate beekeepers in choosing places for the placement of new apiaries and planning activities of an organizational nature.