Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze,...

Area of Science:

• Agricultural Science
• Remote Sensing
• Plant Physiology

Background:

• Accurate monitoring of rice growth and nitrogen status is crucial for optimizing yield.
• Vegetation indices, like NDVI, offer a non-destructive method for assessing crop health.
• Developing dynamic models is essential for real-time crop management.

Purpose of the Study:

• To develop an optimal canopy vegetation index dynamic model for rice.
• To enable real-time, non-destructive diagnosis of rice growth and nitrogen nutrition.
• To establish a technical approach for achieving higher rice yields.

Main Methods:

• Collected data on normalized difference vegetation index (NDVI), leaf area index (LAI), dry matter (DM), and grain yield (GY) from multiple rice cultivars and nitrogen treatments.
• Analyzed quantitative relationships between NDVI and growth indices, establishing positive correlations.
• Developed a normalized NDVI (RNDVI) simulation model using relative accumulative growing degree days (RAGDD) and a double logistic function.
• Validated the RNDVI dynamic models using independent experimental data for Japonica and Indica rice types.

Main Results:

• A double logistic NDVI dynamic model was established with R2 = 0.8577, accurately predicting canopy NDVI changes throughout the growth period.
• Separate RNDVI dynamic models for Japonica and Indica rice achieved high R2 values (0.8764 and 0.8874, respectively).
• Validation confirmed the models' accuracy (k ≈ 1), precision (R2 > 0.8), and low standard deviation, indicating reliable prediction of crop growth and high-yield populations.

Conclusions:

• The developed RNDVI dynamic models accurately reflect rice crop growth status and predict changes in high-yield populations.
• These models provide a rapid and effective approach for monitoring rice growth and nitrogen nutrition.
• The findings support the use of vegetation index dynamic models for optimizing rice cultivation and maximizing yield.