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 Ito^1,a), Ken’ichi Minamino¹, Shintaro Umeki²

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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); DOI: 10.25046/aj090303

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

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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

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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