Predicting Rice Grain Yield Based on Dynamic Changes in Vegetation Indexes during Early to Mid-Growth Stages

Abstract

Predicting the grain yield during early to mid-growth stages is important for initial diagnosis of rice and quantitative regulation of topdressing. In this study, we conducted four experiments using different nitrogen (N) application rates (0–400 kg N∙ha−1) in three Japonica rice cultivars (Wuyunjing24, Ningjing4, and Lianjing7) grown in Jiangsu province, Eastern China, from 2015–2016. Spectral reflectance data were collected multiple times during early to mid-growth stages using an active mounted sensor (RapidScan CS-45, Holland Scientific Inc., Lincoln, NE, USA). Data were then used to calculate optimal vegetation indexes (normalized difference red edge, NDRE; normalized difference vegetation index, NDVI; ratio vegetation index, RVI; red-edge ratio vegetation index, RERVI), which were used to develop a dynamic change model and in-season grain yield prediction model. The NDRE index was more stable than other indexes (NDVI, RVI, RERVI), showing less standard deviation at the same N fertilizer rate. The R2 of the relationships between leaf area index (LAI), plant nitrogen accumulation (PNA), and NDRE also increased compared to other indexes. These findings suggest that NDRE is suitable for analysis of paddy rice N nutrition. According to real-time series changes in NDRE, the resulting dynamic model followed a sigmoid curve, with a coefficient of determination (R2) >0.9 and relative root-mean-square error <5%. Moreover, the feature platform value (saturation value, SV) of the NDRE-based model accurately predicted the differences between treatments and the final grain yield levels. R2 values of the relationship between SV and yield were >0.7. For every 0.1 increase in SV, grain yield increased by 3608.1 kg·ha−1. Overall, our new dynamic model effectively predicted grain yield at stem elongation and booting stages, providing real-time crop N nutrition data for management of N fertilizer topdressing in rice production.

Estimation of Rice Growth Parameters Based on Linear Mixed-Effect Model Using Multispectral Images from Fixed-Wing Unmanned Aerial Vehicles

“…LAI were immediately measured by using a Li-3000c leaf area meter (Li-Cor., Lincoln, NE, USA) to scan the area of separated fresh green leaves. Based on Equation (1), the LAI of the rice population was calculated to represent the LAI of each plot [35].…”

A Machine Learning Framework to Predict Nutrient Content in Valencia-Orange Leaf Hyperspectral Measurements

“…Remote sensing techniques can be useful for the estimation of plant health conditions, including monitoring the nutritional status [1][2][3][4], the stress response [5][6][7], plant count [8,9], yield prediction [10][11][12], chlorophyll content [13][14][15], pest and disease identification [16,17], and biomass estimation [18], among others. Multisensory data is often used to accomplish this task, including the ones acquired by orbital sensors, aircraft or Unnamed Aerial Vehicle (UAV)-embedded cameras, terrestrial sensors, and field spectroradiometers, known as proximal sensors [19][20][21][22][23].…”

Combining Color Indices and Textures of UAV-Based Digital Imagery for Rice LAI Estimation

“…However, unfavorable weather conditions, such as clouds or fog, may lead to the lack of applicable satellite data, consequently limiting their applications for crop monitoring that requires high temporal and spatial resolutions. For applications in small areas, many ground-based non-imaging sensors, such as GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) and Crop Circle series (Holland Scientific, Lincoln, NE, USA), have also been used on canopy scale to estimate LAI, nitrogen status, and predict crop yield [12,13]. However, the overall cost of using these ground-based sensors needs to be evaluated due to the high labor input and the inefficient use of these sensors [14].…”