Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle

Abstract

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R2 of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation.

A Physio-Morphological Trait-Based Approach for Breeding Drought Tolerant Wheat

“…While NDVI can be the first choice for many breeders, the use of other vegetative indices such as Normalized Difference Red Edge (NDRE) may be another option. The performance of NDRE is better compared to NDVI, overcoming the limitations of NDVI associated with absorptance by the upper canopy and saturation at its maximum value during later growth stages of the crop (Fu et al, 2020).…”

A Review on Drone-Based Data Solutions for Cereal Crops

“…The utilization of spectral features was the most significant while that of the 3D features was found to be the least significant [74]. Various methods for wheat yield estimation were used by Fu et al [167]. With a combination of four VIs from multispectral drone images as input in the study, the RF algorithm provided the best estimation accuracy, followed by ANN and partial least square regression (PLSR) methods.…”

“…RF is popular for being relatively insensitive to classification parameters and usually offers high accuracy [169]. However, there is no consensus on which of these methods yields the best results [64,167,170,171]. Moreover, the application of ML methods with a tremendous volume of drone-based agriculture data is relatively unmatured.…”

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

“…Therefore, the joint utilization of potato multi-period data is subject to certain restrictions [60]. With a clear division of growth stages, more re ned research can be carried out like crops such as rice [61] and wheat [62].…”