Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red–Green–Blue Imagery

Abstract

Unmanned aerial vehicles-collected (UAVs) digital red–green–blue (RGB) images provided a cost-effective method for precision agriculture applications regarding yield prediction. This study aims to fully explore the potential of UAV-collected RGB images in yield prediction of winter wheat by comparing it to multi-source observations, including thermal, structure, volumetric metrics, and ground-observed leaf area index (LAI) and chlorophyll content under the same level or across different levels of nitrogen fertilization. Color indices are vegetation indices calculated by the vegetation reflectance at visible bands (i.e., red, green, and blue) derived from RGB images. The results showed that some of the color indices collected at the jointing, flowering, and early maturity stages had high correlation (R2 = 0.76–0.93) with wheat grain yield. They gave the highest prediction power (R2 = 0.92–0.93) under four levels of nitrogen fertilization at the flowering stage. In contrast, the other measurements including canopy temperature, volumetric metrics, and ground-observed chlorophyll content showed lower correlation (R2 = 0.52–0.85) to grain yield. In addition, thermal information as well as volumetric metrics generally had little contribution to the improvement of grain yield prediction when combining them with color indices derived from digital images. Especially, LAI had inferior performance to color indices in grain yield prediction within the same level of nitrogen fertilization at the flowering stage (R2 = 0.00–0.40 and R2 = 0.55–0.68), and color indices provided slightly better prediction of yield than LAI at the flowering stage (R2 = 0.93, RMSE = 32.18 g/m2 and R2 = 0.89, RMSE = 39.82 g/m2) under all levels of nitrogen fertilization. This study highlights the capabilities of color indices in wheat yield prediction across genotypes, which also indicates the potential of precision agriculture application using many other flexible, affordable, and easy-to-handle devices such as mobile phones and near surface digital cameras in the future.

Phenotypic Traits Estimation and Preliminary Yield Assessment in Different Phenophases of Wheat Breeding Experiment Based on UAV Multispectral Images

“…The accuracy of GY prediction in this study was lower (R 2 = 0.49, rRMSE = 6.07% in grain filling) in comparison with some previous studies. For example, [77] achieved R 2 = 0.93 using linear regression and a VI calculated from RGB-camera data. Using linear regression and data from the same camera as in this study, i.e., Sequoia, [17] modelled yield with maximal R 2 of 0.89.…”

Estimation of potato above-ground biomass based on unmanned aerial vehicle red-green-blue images with different texture features and crop height

“…UAV-based ultrahigh-GDS RGB images are helpful not only for estimating AGB ( Batistoti et al, 2019 ) but also for estimating chlorophyll ( Liu et al, 2021b ), nitrogen content ( Li et al, 2015 ), leaf area index ( Yue et al, 2018a ), and crop yield ( Zeng et al, 2021 ). These studies show that ultrahigh-GDS RGB images are rich in crop canopy surface information for monitoring growth.…”

Comparison of Multi-Methods for Identifying Maize Phenology Using PhenoCams

“…Machine learning methods such as SVM, RF and BP and deep learning approaches such as alexnet and googlenet are excellent models for classification and they can be applied to perform the identification of maize phenology [115][116][117][118][119][120]. Nevertheless, phenocams provide an important approach for monitoring the phenological processes of vegetation, by analyzing the agricultural yield of crops by the high-throughput (high temporal and high spatial) data [121][122][123].…”