Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system

Abstract

Background Aboveground biomass (AGB) is a widely used agronomic parameter for characterizing crop growth status and predicting grain yield. The rapid and accurate estimation of AGB in a non-destructive way is useful for making informed decisions on precision crop management. Previous studies have investigated vegetation indices (VIs) and canopy height metrics derived from Unmanned Aerial Vehicle (UAV) data to estimate the AGB of various crops. However, the input variables were derived either from one type of data or from different sensors on board UAVs. Whether the combination of VIs and canopy height metrics derived from a single low-cost UAV system can improve the AGB estimation accuracy remains unclear. This study used a low-cost UAV system to acquire imagery at 30 m flight altitude at critical growth stages of wheat in Rugao of eastern China. The experiments were conducted in 2016 and 2017 and involved 36 field plots representing variations in cultivar, nitrogen fertilization level and sowing density. We evaluated the performance of VIs, canopy height metrics and their combination for AGB estimation in wheat with the stepwise multiple linear regression (SMLR) and three types of machine learning algorithms (support vector regression, SVR; extreme learning machine, ELM; random forest, RF). Results Our results demonstrated that the combination of VIs and canopy height metrics improved the estimation accuracy for AGB of wheat over the use of VIs or canopy height metrics alone. Specifically, RF performed the best among the SMLR and three machine learning algorithms regardless of using all the original variables or selected variables by the SMLR. The best accuracy ( R 2 = 0.78, RMSE = 1.34 t/ha, rRMSE = 28.98%) was obtained when applying RF to the combination of VIs and canopy height metrics. Conclusions Our findings implied that an inexpensive approach consisting of the RF algorithm and the combination of RGB imagery and point cloud data derived from a low-cost UAV system at the consumer-grade level can be used to improve the accuracy of AGB estimation and have potential in the practical applications in the rapid estimation of other growth parameters.

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

“…The NDVI values explain the spectral properties of the plant canopy, whereas plant height is related to the vertical structure and growth rate of a plant [53,56]. Combining the physical and spectral parameters was successful in grasping spectral and structural information and improved the regression estimation of aboveground biomass [20]. The results show combining NDVI and plant height (NDVIsq_PH) data exhibited up to 10-24% improvement in prediction accuracy of DHY than using either NDVI or plant height alone.…”

“…Current, HY phenotyping is time-consuming and relies mainly on visual scoring. HTP platforms that would improve the speed and accuracy of HY phenotyping have, so far, only been applied at the plot level [20,29], and no one has conducted phenotyping at the individual plant level except on trees [38][39][40]. Various sensor-based assessments of phenotyping at the individual plant level is required to implement molecular breeding techniques, like GS.…”

“…Plant height is another widely used structural and physical parameter used to estimate the biomass of various crops [20,23,53,54]. Previous studies on grasslands indicated moderate to high correlation (R 2 = 0.59-0.81) on predicting dry herbage yield from plant height measurements [45].…”

“…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].…”