Detecting and distinguishing between apicultural plants using UAV multispectral imaging

Papachristoforou, Alexandros; Prodromou, Maria; Hadjimitsis, Diofantos; Christoforou, Michalakis

doi:10.7717/peerj.15065

Cited by 3 publications

(2 citation statements)

References 88 publications

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“…The existing literature has demonstrated that combining SAR and optical data allows for the identification of different physical and spectral characteristics of land cover, potentially enhancing classification outcomes [48,[66][67][68][69][70]. Moreover, the use of spectral indices has been shown to improve classification [48,[71][72][73].…”

Section: Methodsmentioning

confidence: 99%

Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features

Prodromou,

Theocharidis,

Gitas

et al. 2024

Remote Sensing

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Accurate mapping of forest habitats, especially in NATURA sites, is essential information for forest monitoring and sustainable management but also for habitat characterisation and ecosystem functioning. Remote sensing data and spatial modelling allow accurate mapping of the presence and distribution of tree species and habitats and are valuable tools for the long-term assessment of habitat status required by the European Commission. In order to serve the above, the present study aims to propose a methodology to accurately map the spatial distribution of forest habitats in three NATURA2000 sites of Cyprus by employing Sentinel-1 and Sentinel-2 data as well as topographic features using the Google Earth Engine (GEE). A pivotal aspect of the methodology identified was that the best band combination of the Random Forest (RF) classifier achieves the highest performance for mapping the dominant habitats in the three case studies. Specifically, in the Akamas region, eight habitat types have been mapped, in Paphos nine and six in Troodos. These habitat types are included in three of the nine habitat groups based on the EU’s Habitat Directive: the sclerophyllous scrub, rocky habitats and caves and forests. The results show that using the RF algorithm achieves the highest performance, especially using Dataset 6, which is based on S2 bands, spectral indices and topographical features, and Dataset 13, which includes S2, S1, spectral indices and topographical features. These datasets achieve an overall accuracy (OA) of approximately 91–94%. In contrast, Dataset 7, which includes only S1 bands and Dataset 9, which combines S1 bands and spectral indices, achieve the lowest performance with an OA of approximately 25–43%.

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Section: Methodsmentioning

confidence: 99%

Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features

Prodromou,

Theocharidis,

Gitas

et al. 2024

Remote Sensing

Self Cite

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“…Interesting observations about detecting and distinguishing apicultural plants Thymus capitatus and Sarcopoterium spinosum, using UAV, were made in [26], whose training method distinguished the two plants with 70% accuracy. The optimal flight parameters and processing options for the detection of taxus and olive trees with UAV-Borne Imager are presented in [27].…”

Section: Introductionmentioning

confidence: 99%

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

Atanasov,

Evstatiev,

Vladut

et al. 2024

AgriEngineering

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

Atanasov,

Evstatiev,

Atanasov

et al. 2024

Diversity

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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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Detecting and distinguishing between apicultural plants using UAV multispectral imaging

Cited by 3 publications

References 88 publications

Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features

Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features

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

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

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