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. 2020 Sep 18;20(18):5354.
doi: 10.3390/s20185354.

Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management

Chin-Ying Yang  1 , Ming-Der Yang  2   3 , Wei-Cheng Tseng  2 , Yu-Chun Hsu  2   3 , Guan-Sin Li  1 , Ming-Hsin Lai  4 , Dong-Hong Wu  1   4 , Hsiu-Ying Lu  5
Affiliations

Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management

Chin-Ying Yang et al. Sensors (Basel). .

Abstract

Rice is one of the three major crops in the world and is the major crop in Asia. Climate change and water resource shortages may result in decreases in rice yields and possible food shortage crises. In this study, water-saving farming management was tested, and IOT field water level monitoring was used to regulate water inflow automatically. Plant height (PH) is an important phenotype to be used to determine difference in rice growth periods and yields using water-saving irrigation. An unmanned aerial vehicle (UAV) with an RGB camera captured sequential images of rice fields to estimate rice PH compared with PH measured on site for estimating rice growth stages. The test results, with two crop harvests in 2019, revealed that with adequate image calibration, the correlation coefficient between UAV-PH and field-PH was higher than 0.98, indicating that UAV images can accurately determine rice PH in the field and rice growth phase. The study demonstrated that water-saving farming is effective, decreasing water usage for the first and second crops of 2019 by 53.5% and 21.7%, respectively, without influencing the growth period and final yield. Coupled with an automated irrigation system, rice farming can be adaptive to water shortage situations.

Keywords: UAV; image processing; irrigation; plant height.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The location of the study area and rice crop in different season. (a) Study area located in Taichung, Taiwan. The orthomosaic image produced from the unmanned aerial vehicle (UAV) flight in 2019; (b) first crop season; and (c) second crop season.
Figure 2
Figure 2
Schematic diagram of water treatment in the test field, Taiwan Agricultural Research Institute, Council of Agriculture. Green areas are experimental rice paddies in this study.
Figure 3
Figure 3
Overview of field plant height survey. (a) Plant height measured from soil surface to plant top using rulers; (b) Plant height generated by UAV.
Figure 4
Figure 4
Workflow of plant height generated by UAV images including (a) DSM generation and (b) plant height model generation.
Figure 5
Figure 5
(a) Original UAV image; (b) highlight image; (c) diffuse image; and (d) reflection image.
Figure 6
Figure 6
An overview of kriging for image interpolation. (a) DSM generated by UAV images; (b) modified DSM by kriging interpolation based on ground control points; and (c) generated DEM by kriging interpolation based on bare ground points.
Figure 7
Figure 7
Comparison between the mean plant height derived from UAV measurements and plant height measured manually of TNG71 rice variety in the field under different water treatment (conventional planting (CP) and alternative wet and dry (AWD)) in (a) the first crop season; and (b) the second crop season.
Figure 8
Figure 8
Correlation between UAV plant height and field plant height.
Figure 9
Figure 9
The cultivation calendar of rice cultivar TNG71 and the variation of water level and rainfall during days after transplant under different water treatments in (a) the first crop season; and (b) the second crop season.
Figure 10
Figure 10
(a) Field irrigation; (b) yield of TNG71 under different water treatments in the first and second crop seasons.
Figure 11
Figure 11
Plant height and the variation of the highest and lowest temperature with cultivation processes in (a) the first crop season; and (b) the second crop season.
Figure 12
Figure 12
The growing degree days of TNG711 until panicle initiation and rainfall in the first and second crop seasons.
Figure 13
Figure 13
Comparison between UAV plant height and field plant height with developmental stages in (a) the first crop season; and (b) the second crop season.
Figure 14
Figure 14
Rice growth rates during three developmental stages in (a) 2019 I; and (b) 2019 II.
Figure 15
Figure 15
Street light causing over-growing PH and preventing from blooming in CP-I in the first crop season.
Figure 16
Figure 16
Low soil levels causing rice death in AWD-I in the second crop season.
Figure 17
Figure 17
Standard deviation of UAV plant height in (a) the first and (b) the second crop seasons under different water treatments (CP and AWD).
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