Estimating Above-Ground Biomass of Maize Using Features Derived from UAV-Based RGB Imagery

Abstract

The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

“…Total biomass is often separated into belowground and aboveground biomass (AGB). Measurements of AGB are logistically easier to collect and are valuable in both agricultural [3][4][5] and non-agricultural settings [6][7][8][9].…”

Development and Validation of a Model to Combine NDVI and Plant Height for High-Throughput Phenotyping of Herbage Yield in a Perennial Ryegrass Breeding Program

“…Small, low-cost unmanned aerial systems (UAS) and satellite technologies (mainly equipped with multispectral sensors) are mainly deployed to execute the phenotyping task. The UAS system allows proximal sensing of plant phenotypes at a small experimental unit level with high spatial and spectral resolution [20][21][22].Another alternative extensively used to estimate herbage yield is plant height [23][24][25]. Plant structural information metrics may be accessed from terrestrial and aerial sensors.…”

“…Small, low-cost unmanned aerial systems (UAS) and satellite technologies (mainly equipped with multispectral sensors) are mainly deployed to execute the phenotyping task. The UAS system allows proximal sensing of plant phenotypes at a small experimental unit level with high spatial and spectral resolution [20][21][22].…”

Fraction vegetation cover extraction of winter wheat based on RGB image obtained by UAV

“…Finally, the digital surface model and orthophoto was generated by using the inverse distance weighting method. More detailed information about the image mosaic processing based on Pix4DMapper software can be seen in [27,28] . In order to extract the FVC of winter wheat, the estimation model of FVC based on vegetation indices and texture features were established during the green returned stage.…”