利用深度学习检测和评估农村和郊区环境中的白花蜜源树和蜜蜂群落位置

Diversity Pub Date : 2024-09-13 DOI:10.3390/d16090578
Atanas Z. Atanasov, Boris I. Evstatiev, Asparuh I. Atanasov, Ivaylo S. Hristakov
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引用次数: 0

摘要

集约农业造成的杀虫剂环境污染损害了蜂群的发展。蜜蜂是地球上最重要的授粉昆虫之一。保护蜜蜂的方法之一是在人口稠密地区迁移和建造养蜂场。蜜蜂群落发展的一个重要条件是开花植物物种的丰富多样性及其所占区域的大小。本研究开发并演示了一种适用于人口稠密环境的方法,用于检测和区分白色开花蜜源树并统计蜂群数量。该方法基于无人机获取的 RGB 图像和两个卷积神经网络--基于像素的网络用于识别开花区域,基于对象的网络用于识别蜂巢,其准确率分别达到 93.4% 和 95.2%。根据在 Yuper 村(保加利亚)附近进行的一项实验研究,确定了农村和郊区环境中黑刺槐(洋槐)区域的生产潜力。结果表明,在使用无人机扫描的 89.725 平方米中,确定的开花面积为 3.654 平方米,确定的蜂箱数量为 149 个。所提出的方法将有助于养蜂人选择新养蜂场的位置和规划组织性活动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection and Assessment of White Flowering Nectar Source Trees and Location of Bee Colonies in Rural and Suburban Environments Using Deep Learning
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.
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