Visualization of the Effect of Additional Fertilization on Paddy Rice by Time-Series Analysis of Vegetation Indices using UAV and Minimizing the Number of Monitoring Days for its Workload Reduction

Visualization of the Effect of Additional Fertilization on Paddy Rice by Time-Series Analysis of Vegetation Indices using UAV and Minimizing the Number of Monitoring Days for its Workload Reduction

Volume 9, Issue 3, Page No 29-40, 2024

Author’s Name: Taichi Ito1,a), Ken’ichi Minamino¹, Shintaro Umeki²

View Affiliations

¹Graduate School of Software and Information Science, Iwate Prefectural University, Takizawa, 0200611, Japan
²Hanamaki Satellite, Research Center for Industrial Science and Technology, Iwate University, Hanamaki, 0250312, Japan

a)whom correspondence should be addressed. E-mail: s236w001@s.iwate-pu.ac.jp

Adv. Sci. Technol. Eng. Syst. J. 9(3), 29-40(2024); a  DOI: 10.25046/aj090303

Keywords: Additional Fertilization, Machine Learning, Paddy Rice, Time-Series Clustering, Unmanned Aerial Vehicle, Vegetation Index

Share
Download Now!

41 Downloads

Export Citations

Abstract

This research is an extension of the research (ISEEIE 2023), which dealt with Time-Series Clustering (TSC) of Vegetation Index (VI) for paddy rice. The novelty of this research is “Visualization of growth changes before and after additional fertilization,” “Analyzing the appropriate amount of additional fertilizer,” and “Optimization of monitoring period to minimize the number of monitoring days for its workload reduction using Unmanned Aerial Vehicle (UAV).” For visualization of growth changes before and after fertilization, a UAV was used to obtain VIs for each mesh and make its time-series data during the monitoring period. Then, TSC was performed on the data. As a result of clustering, NDVI and NDRE increased with additional fertilizer, making it possible to visualize the fertilizer effects. For analyzing the appropriate amount of fertilizer, the its amount applied was changed for each paddy field (2.8, 3.5, 4.2g/m2). In a field experiment conducted, both the TSC results and the crop estimates by unit acreage sampling for each paddy field revealed no difference in yield among fields, indicating that the paddy field with the least fertilizer amount (2.8g/m2) is optimal. It was estimated that this would reduce nitrate nitrogen, which is harmful to soil and the human body, by 0.070mg/L. In addition, for optimization of the monitoring period, the importance of each independent variable outputted by Random Forest (RF) was used to find a subset of monitoring dates. In any VI, there is a period, determined by the range of effective accumulated temperature, when the clustering result does not change even if the number of monitoring dates is reduced (The period could be reduced from 30 to 40 days, which is particularly important for three vegetation indices). From these results, the technologies can help reduce fertilizer costs, excessive fertilization and environmental impacts, and promote the use of UAV.

Received: 14 March 2024, Revised: 02 May 2024, Accepted: 04 May 2024, Published Online: 25 May 2024

References (36)

  1. T. Ito, K. Minamino and S. Umeki, “Analysis of Vegetation Indices by Time Series Clustering of Drone Rice Monitoring Data,” 3rd International Symposium on Electrical, Electronics and Information Engineering (ISEEIE 2023), 2023, DOI: 10.1049/icp.2023.1853
  2. Ministry of Agriculture, Forestry and Fisheries, “2020 nen nou-rin-gyou census kekka no gaiyou (kakuteichi),” URL: https://www.maff.go.jp/j/tokei/kekka_gaiyou/noucen/2020/index.html, [Accessed 5 February 2024], (article in Japanese).
  3. K. Miyama, “Nou-sakumotsu · dozyou no bunkou-hansya-tokusei – nou- you-chi he no remote sensing gizyutsu no tekiyou-sei (I),” Journal of the Agricultural Engineering Society, Japan, 56(12):1197-1202, 1988, (article in Japanese).
  4. J. L. Hatfield and J. H. Prueger, “Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices,” Remote Sensing, 2(2):562-578, 2010, DOI: 10.3390/rs2020562.
  5. J. Xue and B. Su, “Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications,” Journal of Sensors, 2017:1-17, 2017, DOI: 10.1155/2017/1353691 T. Ito et al. / Advances in Science, Technology and Engineering Systems Journal Vol. 9, No. 3, 29-40 (2024) www.astesj.com 40
  6. K. Tanaka and A. Kondoh, “Mapping of Rice Growth Using Low Altitude Remote Sensing by Multicopter,” Journal of The Remote Sensing Society of Japan, 36(4):373-387, 2016, DOI: 10.11440/rssj.39.S1.
  7. K. Harashina, K. Yamamoto, M. Maki, Y. Muto and E. Kurashima, “Monitoring Growth of Water-seeded Rice in Tsunami-stricken Paddy Fields Using UAV-mounted Multispectral Sensor,” Journal of the Japanese Society of Irrigation, Drainage and Rural Engineering, 87(2):121-126, 2019, (article in Japanese with a title in English).
  8. A. Matsuzaki, Inasaku dai hyakka dai 2 han dai 3 kan saibai no zissai/sehi- gizyutsu, Rural Culture Association, 2004, (article in Japanese).
  9. Kubota Co., Ltd., “Mizu to hiryou ni yoru control | tanbo no kanri to higai- taisaku | okome ga dekirumade | Kubota no tanbo [manannde tanoshii! tanbo no sougou zyouhou site],” URL: https://www.kubota.co.jp/kubotatanbo/rice/management/manure.html, [Accessed 21 December 2023], (article in Japanese).
  10. National Institute of Science and Technology Policy, “NISTEP PDF cover 200612,” URL: https://nistep.repo.nii.ac.jp/record/5536/files/NISTEP- STT069.pdf, [Accessed 21 December 2023], (article in Japanese).
  11. Ministry of the Environment, “Suishitsu-osen ni kakawaru kankyo-kizyun,” URL: https://www.env.go.jp/kijun/mizu.html, [Accessed 7 February 2024], (article in Japanese).
  12. T. Ito, K. Minamino and S. Umeki, “Analysis of Vegetation Indexes by Time Series Clustering of Drone Rice Monitoring Data,” The 21st Forum on Information Technology (FIT2022), 21(4):105-110, 2022, (article in Japanese with a title in English).
  13. T. Ito, K. Minamino and S. Umeki, “Analysis of Fertilization Effects on Rice and Wheat by Time-Series Clustering of Vegetation Index Data,” Proceedings of the 5th International Symposium on Advanced Technologies and Applications in the Internet of Things (ATAIT 2023), 2023.
  14. Da-Jiang Innovations Science and Technology Co., Ltd., “AGRAS MG-1S Series – DJI,” URL: https://www.dji.com/mg-1s, [Accessed 8 February 2024].
  15. Da-Jiang Innovations Science and Technology Co., Ltd., “P4 MultispectralDJI,” URL: https://www.dji.com/p4-multispectral, [Accessed 8 February 2024].
  16. A. Karnieli, N. Agam, R. T. Pinker, M. Anderson, M. L. Imhoff, G. G. Gutman, N. Panov and A. Goldberg, “Use of NDVI and Land Surface Temperature for Drought Assessment: Merits and Limitations,” Journal of Climate, 23(3):618-633, 2010, DOI: 10.1175/2009JCLI2900.1
  17. H. Zheng, W. Ji, W. Wang, J. Lu, D. Li, C. Guo, X. Yao, Y. Tian, W. Cao,
    Y. Zhu and T. Cheng, “Transferability of Models for Predicting Rice Grain Yield from Unmanned Aerial Vehicle (UAV) Multispectral Imagery across Years, Cultivars and Sensors,” Drones, 6(12:423):1-19, 2022, DOI: 10.3390/drones6120423.
  18. L. Osborn, “NDVI vs. NDRE: What’s the Difference? – Sentera,” URL: https://sentera.com/resources/articles/ndvi-vs-ndre-whats-the-difference/, [Accessed 8 February 2024].
  19. E. Kanda, Y. Torigoe and T. Kobayashi, “A Model to Estimate the Increase of Leaf Number on the Main Culm of the Rice Plant,” Japanese Journal of Crop Science, 69(4):540-546, 2000, DOI: 10.1626/jcs.69.540, (article in Japanese with an abstract in English).
  20. E. Kanda, Y. Torigoe and T. Kobayashi, “A Simple Model to Predict the Developmental Stages of Rice Panicles Using the Effective Accumulative Temperature,” Japanese Journal of Crop Science, 71(3):394-402, 2002, DOI: 10.1626/jcs.71.394, (article in Japanese with an abstract in English).
  21. A. K. Jain, M. N. Murty and P. J. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, 31(3):264-323, 1999, DOI:10.1145/331499.331504.
  22. R. Tavenard, “tslearn.clustering.TimeSeriesKMeans — tslearn 0.6.3 documentation,”URL:https://tslearn.readthedocs.io/en/stable/gen_modules/clus tering/tslearn.clu stering.TimeSeriesKMeans.html
  23. C. Shi, B. Wei, S. Wei, W. Wang, H. Liu and J. Liu, “A quantitative discriminant method of elbow point for the optimal number of clusters in clustering algorithm,” EURASIP Journal on Wireless Communications and Networking, 31:1-16, 2021, DOI: 10.1186/s13638-021-01910-w.
  24. S. Ronaghan, “The Mathematics of Decision Trees, Random Forest and Feature Importance in Scikit-learn and Spark,” URL: https://towardsdatascience.com/the-mathematics-of-decision-trees- random- forest-and-feature-importance-in-scikit-learn-and-spark-f2861df67e3, [Accessed 11 February 2024].
  25. The IDB Project, “Index DataBase,” URL: https://www.indexdatabase.de/, [Accessed 23 April 2024].
  26. Toyama Prefecture, “Chika-sui kanyou no suishin ni mukete ,” URL: https://www.pref.toyama.jp/documents/7739/00745883.pdf, [Accessed 17 February 2024], (article in Japanese).
  27. C. Imagawa, “Nitrogen Dynamics Modeling to Support Land and Water Management in Rice Paddy Watershed,” Soil/Water Research Group Materials, 30, 2013, (article in Japanese with a title in English).
  28. M. Tsuji and M. Takei, “Degrees of Water Purification in Cultivated Rice Fields and Fallow Fields,” Research bulletin of the Aichi-ken Agricultural Research Center, 43:1-6, 2011, (article in Japanese with an abstract in English).
  29. M. M. Tahat, K. M. Alananbeh, Y. A. Othman and D. I. Leskovar, “Soil Health and Sustainable Agriculture,” Sustainability, 12(12), 2020, DOI: 10.3390/su12124859.
  30. NIKON-TRIMBLE CO., LTD.,”GreenSeeker 2,” URL:https://www.nikon-trimble.co.jp/products/product_detail.html?tid=428, [Accessed 15 February 2024].
  31. Y. Kaneta, M. Nishida, F. Takakai and T. Sato, “New growth diagnosis standards of high-yielding rice and demonstration of high yielding using NDVI by GreenSeeker handheld crop sensor,” Japanese Journal of Soil Science and Plant Nutrition, 91(6):417-425, 2020, DOI: 10.20710/dojo.91.6_417, (article in Japanese with an abstract in English).
  32. S. Ayoub, Y. Gulzar, F. A. Reegu and S. Turaev, “Generating Image Captions Using Bahdanau Attention Mechanism and Transfer Learning,” Symmetry, 14(12), 2022, DOI: 10.3390/sym14122681.
  33. F. A. Reegu, H. Abas, Y. Gulzar, Q. Xin, A. A. Alwan, A. Jabbari, R. G. Sonkamble and R. A. Dziyauddin, “Blockchain-Based Framework for Interoperable Electronic Health Records for an Improved Healthcare System,” Sustainability, 15(8), 2023, DOI: 10.3390/su15086337.
  34. A. A. Dar, M. Z. Alam, A. Ahmad, F. A. Reegu and S. A. Rahin, “Blockchain Framework for Secure COVID-19 Pandemic Data Handling and Protection,” Computational Intelligence and Neuroscience, 2022(7025485):1-11, 2022, DOI: 10.1155/2022/7025485.
  35. M. Z. Alam, F. Reegu, A. A. Dar and W. A. Bhat, “Recent Privacy and Security Issues in Internet of Things Network Layer: A Systematic Review,” 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), 2022, DOI: 10.1109/ICSCDS53736.2022.9760927.
  36. F. A. Reegu, W. A. Bhat, A. Ahmad and M. Z. Alam, “A review of importance of blockchain in IOT security,” International Conference on Emerging Trends in Materials, Computing and Communication Technologies (ICETMCCT 2021), 2587(1), 2023, DOI: 10.1063/5.0150432.

Cited By

Citations by Dimensions

Citations by PlumX

Crossref Citations

This paper is currently not cited.

Metrics

No. of Downloads Per Month

No. of Downloads Per Country