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

Abstract

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI=(1+e−15.2829×(RAGDDi−0.1944))−1−(1+e−11.6517×(RAGDDi−1.0267))−1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status.

Effect of Cultivar on Chlorophyll Meter and Canopy Reflectance Measurements in Cucumber

“…Monostori et al [30] reported for wheat that SPAD values should be calibrated for each cultivar to obtain more accurate N diagnosis and yield prediction. The present work and previous research [46,47] suggest that in order to use absolute sufficiency values, regardless of the cultivar, that procedures to normalize absolute chlorophyll meter measurements should be developed.…”

Development of Chlorophyll-Meter-Index-Based Dynamic Models for Evaluation of High-Yield Japonica Rice Production in Yangtze River Reaches

“…We examined N distribution by evaluating color differences between upper and lower leaves, and via differential diagnosis of rice N status. A previous study found that chlorophyll content reflected N nutritional status, but was significantly influenced by variety, site, and year of experimentation [31]. A previous study indicated that data normalization prior to modeling eliminated differences caused by variety, soil types, and management strategies, etc.…”

“…Calculating relative SPAD values requires the use of a non-N-limiting treatment as a control, reducing the utility of the method regarding on-farm N diagnosis [30]. SPAD value differences among varieties, and variations in production levels, can be eliminated by normalizing experimental data before modeling [31]. Therefore, some studies have used different normalized SPAD index (NDSI) values to reduce the effects of variation.…”

“…In recent years, many authors have sought to simulate the crop tiller number (LAI) and other dynamic indicators [31]. Dynamic models of crop growth indices should ideally be universal [33], but labor-and time-consuming due to destructive sampling required for obtaining the LAI, dry matter (DM), tissue N concentration, and other growth parameters.…”

Chlorophyll meter–based nitrogen fertilizer optimization algorithm and nitrogen nutrition index for in‐season fertilization of paddy rice

“…Several attempts have been made to eliminate the influence of factors that affect SPAD readings by normalizing the SPAD reading (Liu et al., ), which can overcome the negative influences of cultivars, locations, years, and growing stages (Wang, Zhu, Jiang, & Cao, ). However, N regulation strategies based on normalized SPAD values have rarely been reported.…”