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http://dx.doi.org/10.5532/KJAFM.2022.24.4.295

Sorghum Panicle Detection using YOLOv5 based on RGB Image Acquired by UAV System  

Min-Jun, Park (Department of Bio-System Engineering, Gyengsang National University (Institute of Agriculture & Life Science))
Chan-Seok, Ryu (Department of Bio-System Engineering, Gyengsang National University (Institute of Agriculture & Life Science))
Ye-Seong, Kang (Department of Bio-System Engineering, Gyengsang National University (Institute of Agriculture & Life Science))
Hye-Young, Song (Department of Bio-System Engineering, Gyengsang National University (Institute of Agriculture & Life Science))
Hyun-Chan, Baek (Department of Bio-System Engineering, Gyengsang National University (Institute of Agriculture & Life Science))
Ki-Su, Park (Department of Bio-System Engineering, Gyengsang National University (Institute of Agriculture & Life Science))
Eun-Ri, Kim (Department of Bio-System Engineering, Gyengsang National University (Institute of Agriculture & Life Science))
Jin-Ki, Park (Southern Crop Department, National Institute of Crop Science, Rural Development Administration)
Si-Hyeong, Jang (Fruit Research Division, National institute of Horticultural & Herbal Science)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.24, no.4, 2022 , pp. 295-304 More about this Journal
Abstract
The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.
Keywords
Sorghum; UAV; RGB; YOLO; Detection;
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1 Alzubaidi, L., J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaria, M. A. Fadhel, M. Al-Amidie, and L. Farhan, 2021: Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of big Data 8(1), 1-74.   DOI
2 Bayraktar, E., M. E. Basarkan, and N. Celebi, 2020: A low-cost UAV framework towards ornamental plant detection and counting in the wild. ISPRS Journal of Photogrammetry and Remote Sensing 167, 1-11.   DOI
3 David, E., G. Daubige, F. Joudelat, P. Burger, A. Comar, B. de Solan, and F. Baret, 2022: Plant detection and counting from high-resolution RGB images acquired from UAVs: comparison between deep-learning and handcrafted methods with application to maize, sugar beet, and sunflower. bioRxiv, 2021-04.
4 Du, Z., J. Yang, C. Ou, and T. Zhang, 2019: Smallholder crop area mapped with a semantic segmentation deep learning method. Remote Sensing 11(7), 888.   DOI
5 Garcia-Martinez, H., H. Flores-Magdaleno, A. Khalil-Gardezi, R. Ascencio-Hernandez, L. Tijerina-Chavez, M. A. Vazquez-Pena, and O. R. Mancilla-Villa, 2020: Digital count of corn plants using images taken by unmanned aerial vehicles and cross correlation of templates. Agronomy 10(4), 469.   DOI
6 Gonzalo-Martin, C., A. Garcia-Pedrero, and M. Lillo-Saavedra, 2021: Improving deep learning sorghum head detection through test time augmentation. Computers and Electronics in Agriculture 186, 106179.   DOI
7 Ha, Y. D., and S. P. Lee, 2001: Characteristics of proteins in Italian millet, sorghum and common millet. Korean J Postharvest Sci Technol 8(2), 187-192.
8 NICS (National Institute of Crop Science), 2016: http://www.nics.go.kr/bbs/view.do?m=100000126&bbsId=research&bbsSn=202912
9 Stehr, N. J., 2015: Drones: The newest technology for precision agriculture. Natural Sciences Education, 44(1), 89-91.   DOI
10 Tack, J., J. Lingenfelser, and S. K. Jagadish, 2017: Disaggregating sorghum yield reductions under warming scenarios exposes narrow genetic diversity in US breeding programs. Proceedings of the National Academy of Sciences 114(35), 9296-9301.   DOI
11 Yang, F., and M. Wang, 2021: Deep learning-based method for detection of external air conditioner units from street view images. Remote Sensing 13(18), 3691.   DOI
12 Yang, S., L. Hu, H. Wu, H. Ren, H. Qiao, P. Li, and W. Fan, 2021: Integration of crop growth model and random forest for winter wheat yield estimation from UAV hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, 6253-6269.   DOI
13 Zhang, Y., M. Li, X. Ma, X. Wu, and Y. Wang, 2022: High-precision wheat head detection model based on one-stage network and GAN model. Frontiers in Plant Science 13.
14 KOSIS (Korea Statistical Information Service), 2022: Grain Production, https://kosis.kr/
15 Zhao, J., X. Zhang, J. Yan, X. Qiu, X. Yao, Y. Tian, Y. Zhu, and W. Cao, 2021: A wheat spike detection method in UAV images based on improved YOLOv5. Remote Sensing 13(16), 3095.   DOI
16 NICS (National Institute of Crop Science), 2019: http://www.nics.go.kr/bbs/view.do?m=100000126&bbsId=research&bbsSn=463112
17 KMA (Korea Meteorological Administration), 2022: https://data.kma.go.kr/data/grnd/selectAsosRltmList.do?pgmNo=36
18 Li, H., P. Wang, and C. Huang, 2022: Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery. Remote Sensing 14(13), 3143.   DOI
19 Marsalis, M. A., S. V. Angadi, and F. E. Contreras-Govea, 2010: Dry matter yield and nutritive value of corn, forage sorghum, and BMR forage sorghum at different plant populations and nitrogen rates. Field Crops Research 116(1-2), 52-57.   DOI
20 MAFRA (Ministry of Agriculture, Food and Rural Affairs of Korea), 2021: 양정자료. 36-37, 11-1543000-000002-10
21 Moon, H., J. H. Ryu, S. I. Na, S. W. Jang, S. H. Sin, and J. Cho, 2021: Comparative Analysis of Rice Lodging Area Using a UAV-based Multispectral Imagery. Korean Journal of Remote Sensing 37(5_1), 917-926.   DOI
22 Na, S., C. Park, K. So, H. Ahn, and K. Lee, 2018: Application method of unmanned aerial vehicle for crop monitoring in Korea. Korean Journal of Remote Sensing 34(5), 829-846.   DOI
23 Prasad, P. V., and S. A. Staggenborg, 2009: Growth and production of sorghum and millets. Soils, plant growth and crop production 2.
24 RDA (Rural Development Administration of Korea), 2020: A study on the Management and Profitability of Small Area Cultivated Crops
25 Randelovic, P., V. Dordevic, S. Milic, S. Balesevic-Tubic, K. Petrovic, J. Miladinovic, and V. Dukic, 2020: Prediction of soybean plant density using a machine learning model and vegetation indices extracted from RGB images taken with a UAV. Agronomy 10(8), 1108.   DOI
26 Redmon, J., S. Divvala, R. Girshick, and A. Farhadi, 2016: You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, 779-788.