# Assessment of the Rice Panicle Initiation by Using NDVI-Based Vegetation Indexes
###### Abstract  
The assessment of rice panicle initiation is crucial for the management of nitrogen fertilizer application that affects yield and quality of grain. The occurrence of panicle initiation could be determined via either green ring, internode-elongation, or a 1-2 mm panicle, and was observed through manual dissection. The quadratic polynomial regression model was used to construct the model of the trend of normalized difference vegetation index-based vegetation indexes (NDVI-based VIs) between pre-tillering and panicle differentiation stages. The slope of the quadratic polynomial regression model tended to be alleviated in the period in which the panicle initiation stage should occur. The results indicated that the trend of the NDVI-based VIs was correlated with panicle initiation. NDVI-based VIs could be a useful indicator to remotely assess panicle initiation. hyperspectral; proximal sensing; panicle initiation; normalized difference vegetation index (NDVI); green ring; internode-elongation +
Footnote †: journal: Applied Sciences  
**Citation**: [PERSON]; [PERSON] Assessment of the Rice Panicle Initiation by Using NDVI-Based Vegetation Indexes. _Appl. Sci._**2021**, _II_, 10076. [[https://doi.org/10.3390/app112110076](https://doi.org/10.3390/app112110076)]([https://doi.org/10.3390/app112110076](https://doi.org/10.3390/app112110076))  
Academic Editor [PERSON] and [PERSON]  
Received: 4 October 2021  
Accepted: 25 October 2021  
Published: 27 October 2021  
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weather variability during the cultivation period [7; 8; 9]. Growing degree day (GDD) is an excellent heat unit that has been widely applied in corn production [10] since it was first proposed to describe the timeline of biological development [7]. However, these methods are rudimentary and are not able to distinguish the variability between fields. Nowadays, quantitative assessment, real-time and site-specific management of precision agriculture are the objectives of community. Spectral remote sensing is another potential approach for the estimation of various variables that are correlated to plant architecture and physiology, providing high-throughput information non-destructively and rapidly for precision agriculture. The arithmetic combinations of vegetation spectral reflectance, which usually termed as vegetation indexes (VIs), became useful indicators for studying plant health and status. The NDVI (normalized difference vegetation index), which is calculated through a normalization procedure [11], seems to be the most popular and long-established VI. The NDVI is sensitive to responses on the green vegetation [12] as it correlates with the biophysical and physiological changes in plants [13; 14; 15; 16; 17; 18]. Moreover, the NDVI has been subsequently developed to the other NDVI-based VIs, such as NDRE (normalized differential red-edge), GNDVI (green NDVI), NDSI (normalized difference spectral index), etc. [19; 20; 21]. Recent studies put efforts on the quantification of nitrogen contents in plants through remote sensing to optimize the fertilization efficiency and increased the yield [22; 23; 24; 25; 26]. In the field of rice research, NDVI is one of the most frequently used vegetation indexes. For example, preceding studies revealed that the NDVI is useful for in the research of rice breeding, nitrogen use efficiency monitoring, and rice yield prediction [27; 28; 29]. Furthermore, NDVI has also been used to monitor the rice growth stages, including the panicle development stage [30]. The preceding studies revealed the trend of NDVI changes during the rice growth cycle; however, the evaluation of PI prediction is currently unavailable. The main objective of this study, therefore, is to investigate the relationship between NDVI and PI occurrence.