Detecting and distinguishing between apicultural plants using UAV multispectral imaging.

Autor:	Papachristoforou A; Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, Thessaloniki, Greece.; Department of Food Science and Nutrition, School of the Environment, University of the Aegean, Myrina, Greece., Prodromou M; Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus.; Department of Environment and Climate, Eratosthenes Center of Excelence, Limassol, Cyprus., Hadjimitsis D; Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol, Cyprus.; Department of Environment and Climate, Eratosthenes Center of Excelence, Limassol, Cyprus., Christoforou M; Department of Environment and Climate, Eratosthenes Center of Excelence, Limassol, Cyprus.; Department of Agricultural Science, Biotechnology and Food Science, Cyprus University of Technology, Limassol, Cyprus.
Jazyk:	angličtina
Zdroj:	PeerJ [PeerJ] 2023 Apr 14; Vol. 11, pp. e15065. Date of Electronic Publication: 2023 Apr 14 (Print Publication: 2023).
DOI:	10.7717/peerj.15065
Abstrakt:	Detecting and distinguishing apicultural plants are important elements of the evaluation and quantification of potential honey production worldwide. Today, remote sensing can provide accurate plant distribution maps using rapid and efficient techniques. In the present study, a five-band multispectral unmanned aerial vehicle (UAV) was used in an established beekeeping area on Lemnos Island, Greece, for the collection of high-resolution images from three areas where Thymus capitatus and Sarcopoterium spinosum are present. Orthophotos of UAV bands for each area were used in combination with vegetation indices in the Google Earth Engine (GEE) platform, to classify the area occupied by the two plant species. From the five classifiers (Random Forest, RF; Gradient Tree Boost, GTB; Classification and Regression Trees, CART; Mahalanobis Minimum Distance, MMD; Support Vector Machine, SVM) in GEE, the RF gave the highest overall accuracy with a Kappa coefficient reaching 93.6%, 98.3%, 94.7%, and coefficient of 0.90, 0.97, 0.92 respectively for each case study. The training method used in the present study detected and distinguish the two plants with great accuracy and results were confirmed using 70% of the total score to train the GEE and 30% to assess the method's accuracy. Based on this study, identification and mapping of Thymus capitatus areas is possible and could help in the promotion and protection of this valuable species which, on many Greek Islands, is the sole foraging plant of honeybees. Competing Interests: The authors declare there are no competing interests. (©2023 Papachristoforou et al.)
Databáze:	MEDLINE
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