Evaluation of Planting Distance in Rice Paddies Using Deep Learning-Based Drone Imagery

Hyeok-jin Bak; Dongwon Kwon; Woo-jin Im; Ji-hyeon Lee; Eun-ji Kim; Nam-jin Chung; Jung-Il Cho; Woon-Ha Hwang; Jae-Ki Chnag; Wan-Gyu Sang

doi:10.7740/kjcs.2024.69.3.154

All Issue

2024 Vol.69, Issue 3 Preview Page Next Page

Original Research Article

Evaluation of Planting Distance in Rice Paddies Using Deep Learning-Based Drone Imagery 딥 러닝 기반 드론 영상을 활용한 벼 포장의 재식거리 평가

1 September 2024. pp. 154-162

PDF XML

Abstract

In response to the increasing impact of climate change on agriculture, various cultivation technologies have been recently developed to improve agricultural productivity and reduce carbon emissions for carbon neutrality. This study presents an algorithm for estimating rice planting density in agriculture using drone-captured images and deep learning-based image analysis technology. The algorithm utilizes images collected from various paddies; these images are processed through pre-processing steps and serve as training data for the YOLOv5x deep learning model. The trained model demonstrated high precision and recall, effectively estimating the position information of rice plants in each image. By accurately estimating the position of rice plants based on the central coordinates in diverse unpaved environments, the model allowed for estimation of rice plant density in each paddy, producing values closely aligned with actual measurements. Moreover, the algorithm proposed in this study provides a novel approach for precise determination of rice planting density based on the position information of rice plants in the images. Analysis of drone footage from different regions capturing portions of paddies revealed that the developed algorithm exhibited a significant correlation (R²=0.877) with actual planting density. This finding suggests the potential effective application of the algorithm in real-world agricultural settings. In conclusion, we believe that this research contributes to the ongoing digital transformation in agriculture by offering a valuable technology that supports the goals of enhancing efficiency, mitigating methane emissions, and achieving carbon neutrality, in response to the challenges posed by climate change.

Keywords

deep learning

high throughput phenotyping

object detection

planting density

rice

UAV image

References

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 : 53. https://doi.org/10.1186/s40537-021-00444-8

10.1186/s40537-021-00444-833816053PMC8010506

Bak, H. J., D. W. Kwon, W. G. Sang, H. Y. Ban, S. Y. Chang, J. K. Beak, Y. H. Lee, W. J. Im, M. C. Seo and J. I. Cho. 2023. Deep learning approaches for accurate weed Area assessment in maize fields. Korean Journal of Agricultural and Forest Meteorology 25(1) : 17-27.

Ban, H. Y., J. K. Beak, W. G. Sang, J. H. Kim, and M. C. Seo. 2021. Estimation of the lodging area in rice using deep learning. Korean J. Crop Sci. 66(2) : 105-111.

Bouguettaya, A., H. Zarzour, A. Kechida, and A. M. Taberkit. 2022. Deep learning techniques to classify agricultural crops through UAVimagery: a review. Neural Computing and Applications 34 : 9511-9536. https://doi.org/10.1007/s00521-022-07104-9

10.1007/s00521-022-07104-935281624PMC8898032

Dobosz, B., D. Gozdowski, J. Koronczok, J. Žukovskis, and E. Wójcik-Gront. 2023. Evaluation of maize crop damage using UAV-Based RGB and multispectral imagery. Agriculture 13(8) : 1627. https://doi.org/10.3390/agriculture13081627

10.3390/agriculture13081627

Hama, A., K. Tanaka, B. Chen, and A. Kondoh. 2021. Examination of appropriate observation time and correction of vegetation index for drone-based crop monitoring. Journal of Agricultural Meteorology 77(3) : 200-209. https://doi.org/10.2480/agrmet.D-20-00047

10.2480/agrmet.D-20-00047

Hayashi, S., A. Kamoshita, and J. Yamagishi. 2006. Effect of planting density on grain yield and water productivity of rice grown in flooded and non-flooded fields in Japan. Journal of Plant Prod. Sci. 9(3) : 298-311.

10.1626/pps.9.298

Hwang, W. H., H. S. Lee, S. Y. Yang, and C. G. Lee. 2023. Change in yield characteristics by transplanting density in major cultivated rice. Korean J. Crop Sci. 68(1) : 1-7

Kanetaka, M., A. Takahashi, and S. Azuma. 2004. Transplanting culture by dense sowing and sparse planting of Koshihikari. The Hor. Crop Sci. 40 : 11-14.

Kazemi, F. and E. Parmehr. 2022. Evaluation of rgb vegetation indices derived from uav images for rice crop growth monitoring. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 : 385-390. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-385-2023

10.5194/isprs-annals-X-4-W1-2022-385-2023

Kim, G. Y., S. B. Lee, J. S. Lee, E. J. Choi, and J. H. Ryu. 2012. Mitigation of greenhouse gases by water management of SRI (System of Rice Intensification) in rice paddy fields. Korean J. Soil Sci. 45(6) : 1173-1178.

10.7745/KJSSF.2012.45.6.1173

Lee, Y. H., H. J. Bak, J. W. Kim, B. K. Kim, H. S. Jang, D. W. Kim, J. T. Youn, and T. Y. Hwang. 2022. Evaluation of leaf area, canopy coverage light interception, and exposure setting in wheat using RGB color vegetation indices. Journal of Agriculture & Life Science 56(6) : 1-13.

10.14397/jals.2022.56.6.1

Li, B., X. Xu, J. W. Han, L. Zhang, C. Bian, L. Jin, and J. Liu. 2019. The estimation of crop emergence in potatoes by UAV RGB imagery. Plant Methods 15 : 15. https://doi.org/10.1186/s13007-019-0399-7

10.1186/s13007-019-0399-730792752PMC6371461

Norman, U. 2008. The System of Rice Intensification (SRI) as a system of agricultural innovation. Jurnal Tanah dan Lingkungan 10(1) : 27-40

10.29244/jitl.10.1.27-40

Redmon, J., S. Divvala, R. Girshick and A. Farhadi. 2016. You only look once: Unified, Real-Time Object Detection. arXiv: 1506.02640v5.

10.1109/CVPR.2016.91

Shen, J., Q. Wang, M. Zhao, J. Hu, J. Wang, M. Shu, Y. Liu, W. Guo, H. Qiao, Q. Niu and J. Yue. 2024. Mapping maize planting densities using unmanned aerial vehicles, multispectral remote sensing, and deep learning technology. Drones 8, 140. https://doi.org/10.3390/drones8040140

10.3390/drones8040140

Shorten, C. and T. M. Khoshgoftaar. 2019. A survey on image data augmentation for deep learning. Journal of Big Data. 6:60 https://doi.org/10.1186/s40537-019-0197-0

10.1186/s40537-019-0197-0

Ultralytics. 2020. YOLOv5: A family of object detection architectures and models. Ultralytics GitHub Repository. Available at: https://github.com/ultralytics/yolov5

Yang, S. Y., W. H, Hwang, J. H. Jeong, H. S. Lee, and C. G. Lee. 2021. Changes in growth and yield of different rice varieties under different planting densities in low-density transplanting cultivation. Korean J. Crop Sci. 66(4) : 279-288.

Information

Publisher :The Korean Society of Crop Science
Publisher(Ko) :한국작물학회
Journal Title :The Korean Journal of Crop Science
Journal Title(Ko) :한국작물학회지
Volume : 69
No :3
Pages :154-162
Received Date : 2024-07-31
Revised Date : 2024-08-20
Accepted Date : 2024-08-21
DOI :https://doi.org/10.7740/kjcs.2024.69.3.154

The Korean Journal of Crop Science ISSN:0252-9777(Print) 2287-8432(Online) 한국작물학회지

All Issue