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Agronomy
Volume 14
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10.3390/agronomy14020337
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Article

Estimation of Garden Chrysanthemum Crown Diameter Using Unmanned Aerial Vehicle (UAV)-Based RGB Imagery

by
Jiuyuan Zhang
†,
Jingshan Lu
†,
Qiuyan Zhang
,
Qimo Qi
,
Gangjun Zheng
,
Fadi Chen
,
Sumei Chen
,
Fei Zhang
,
Weimin Fang
and
Zhiyong Guan
*
Key Laboratory of Landscaping, Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing 210095, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2024, 14(2), 337; https://doi.org/10.3390/agronomy14020337
Submission received: 28 December 2023 / Revised: 27 January 2024 / Accepted: 1 February 2024 / Published: 6 February 2024
(This article belongs to the Special Issue Smart Strategies and Technologies for Sustainability and Biodiversity in Herbaceous and Horticultural Crops—2nd Edition)
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Abstract

Crown diameter is one of the crucial indicators for evaluating the adaptability, growth quality, and ornamental value of garden chrysanthemums. To accurately obtain crown diameter, this study employed an unmanned aerial vehicle (UAV) equipped with a RGB camera to capture orthorectified canopy images of 64 varieties of garden chrysanthemums at different growth stages. Three methods, namely RGB color space, hue-saturation-value (HSV) color space, and the mask region-based convolutional neural network (Mask R-CNN), were employed to estimate the crown diameter of garden chrysanthemums. The results revealed that the Mask R-CNN exhibited the best performance in crown diameter estimation (sample number = 2409, R2 = 0.9629, RMSE = 2.2949 cm). Following closely, the HSV color space-based model exhibited strong performance (sample number = 2409, R2 = 0.9465, RMSE = 3.4073 cm). Both of the first two methods were efficient in estimating crown diameter throughout the entire growth stage. In contrast, the RGB color space-based model exhibited slightly lower performance (sample number = 1065, R2 = 0.9011, RMSE = 3.3418 cm) and was only applicable during periods when the entire plant was predominantly green. These findings provide theoretical and technical support for utilizing UAV-based imagery to estimate the crown diameter of garden chrysanthemums.
Keywords: garden chrysanthemum; crown diameter; unmanned aerial vehicle (UAV); RGB imagery; mask R-CNN garden chrysanthemum; crown diameter; unmanned aerial vehicle (UAV); RGB imagery; mask R-CNN

Share and Cite

MDPI and ACS Style

Zhang, J.; Lu, J.; Zhang, Q.; Qi, Q.; Zheng, G.; Chen, F.; Chen, S.; Zhang, F.; Fang, W.; Guan, Z. Estimation of Garden Chrysanthemum Crown Diameter Using Unmanned Aerial Vehicle (UAV)-Based RGB Imagery. Agronomy 2024, 14, 337. https://doi.org/10.3390/agronomy14020337

AMA Style

Zhang J, Lu J, Zhang Q, Qi Q, Zheng G, Chen F, Chen S, Zhang F, Fang W, Guan Z. Estimation of Garden Chrysanthemum Crown Diameter Using Unmanned Aerial Vehicle (UAV)-Based RGB Imagery. Agronomy. 2024; 14(2):337. https://doi.org/10.3390/agronomy14020337

Chicago/Turabian Style

Zhang, Jiuyuan, Jingshan Lu, Qiuyan Zhang, Qimo Qi, Gangjun Zheng, Fadi Chen, Sumei Chen, Fei Zhang, Weimin Fang, and Zhiyong Guan. 2024. "Estimation of Garden Chrysanthemum Crown Diameter Using Unmanned Aerial Vehicle (UAV)-Based RGB Imagery" Agronomy 14, no. 2: 337. https://doi.org/10.3390/agronomy14020337

APA Style

Zhang, J., Lu, J., Zhang, Q., Qi, Q., Zheng, G., Chen, F., Chen, S., Zhang, F., Fang, W., & Guan, Z. (2024). Estimation of Garden Chrysanthemum Crown Diameter Using Unmanned Aerial Vehicle (UAV)-Based RGB Imagery. Agronomy, 14(2), 337. https://doi.org/10.3390/agronomy14020337

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