A Novel Algorithm to Detect White Flowering Honey Trees in Mixed Forest Ecosystems Using UAV-Based RGB Imaging

A. Atanasov, Boris I. Evstatiev, Valentin N. Vladut, S. Biriș
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Abstract

Determining the productive potential of flowering vegetation is crucial in obtaining bee products. The application of a remote sensing approach of terrestrial objects can provide accurate information for the preparation of maps of the potential bee pasture in a given region. The study is aimed at the creation of a novel algorithm to identify and distinguish white flowering honey plants, such as black locust (Robinia pseudo-acacia) and to determine the areas occupied by this forest species in mixed forest ecosystems using UAV-based RGB imaging. In our study, to determine the plant cover of black locust in mixed forest ecosystems we used a DJI (Da-Jiang Innovations, Shenzhen, China) Phantom 4 Multispectral drone with 6 multispectral cameras with 1600 × 1300 image resolution. The monitoring was conducted in the May 2023 growing season in the village of Yuper, Northeast Bulgaria. The geographical location of the experimental region is 43°32′4.02″ N and 25°45′14.10″ E at an altitude of 223 m. The UAV was used to make RGB and multispectral images of the investigated forest massifs, which were thereafter analyzed with the software product QGIS 3.0. The spectral images of the observed plants were evaluated using the newly created criteria for distinguishing white from non-white colors. The results obtained for the scanned area showed that approximately 14–15% of the area is categorized as white-flowered trees, and the remaining 86–85%—as non-white-flowered. The comparison of the developed algorithm with the Enhanced Bloom Index (EBI) approach and with supervised Support Vector Machine (SVM) classification showed that the suggested criterion is easy to understand for users with little technical experience, very accurate in identifying white blooming trees, and reduces the number of false positives and false negatives. The proposed approach of detecting and mapping the areas occupied by white flowering honey plants, such as black locust (Robinia pseudo-acacia) in mixed forest ecosystems is of great importance for beekeepers in determining the productive potential of the region and choosing a place for an apiary.
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利用基于无人机的 RGB 成像检测混交林生态系统中白花蜜树的新算法
确定开花植被的生产潜力对获取蜂产品至关重要。应用陆地遥感方法可以为绘制特定地区潜在的蜜蜂牧场地图提供准确的信息。本研究旨在创建一种新型算法,利用基于无人机的 RGB 成像技术识别和区分白花蜜源植物,如黑刺槐(刺槐),并确定混交林生态系统中该森林物种所占据的区域。在我们的研究中,为了确定混交林生态系统中黑刺槐的植被覆盖情况,我们使用了大疆创新公司(中国深圳,大疆创新)的 Phantom 4 多光谱无人机,该无人机配有 6 个多光谱相机,图像分辨率为 1600 × 1300。监测工作于 2023 年 5 月的生长季节在保加利亚东北部的 Yuper 村进行。实验区的地理位置为北纬 43°32′4.02″,东经 25°45′14.10″,海拔 223 米。无人机用于拍摄所调查森林丘陵的 RGB 和多光谱图像,然后使用 QGIS 3.0 软件产品进行分析。观测到的植物的光谱图像使用了新制定的区分白色和非白色的标准进行评估。扫描区域的结果显示,约有 14-15% 的区域被归类为白花树,其余 86-85% 的区域被归类为非白花树。将所开发的算法与增强型开花指数(EBI)方法和有监督的支持向量机(SVM)分类方法进行比较后发现,所建议的标准对于缺乏技术经验的用户来说很容易理解,在识别白花树方面非常准确,并且减少了假阳性和假阴性的数量。所提出的检测和绘制混交林生态系统中黑刺槐等白花蜜源植物所占区域的方法,对于养蜂人确定该地区的生产潜力和选择养蜂场地点具有重要意义。
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