科学家利用Airphen多光谱成像相机系统发表了题为Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle的文章，文章发表于著名期刊Remote Sensing上。

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

Zhaopeng Fu 1 , Jie Jiang 1 , Yang Gao 1 , Brian Krienke 2 , Meng Wang 1 , Kaitai Zhong 1, Qiang Cao 1 , Yongchao Tian 1 , Yan Zhu 1 , Weixing Cao 1 and Xiaojun Liu 1,* 

Abstract and Figures

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. 

Acquisition of UAV Images

This experiment used a six-rotor UAV (DJI M600Pro, Shenzhen, CHN) with a multi

-spectral camera (Airphen, Hiphen, FR) to obtain image data at an altitude of 50 m above the wheat canopy (spatial resolution was 4.7 cm, focal length was 8 mm, heading overlap was 85%, sideways overlap was 90%, flflight speed was 2 m/s). The acquisition of UAV images was synchronized with fifield sampling time (Table 1). We used the software DJI GS PRO (https://www.dji.com/cn/ground -station-pro/) to pre-plan the route and monitor the UAV’s flflight. The multi-spectral camera was composed of six channels with a resolution of 1280 × 960 and wavelengths of 450, 530, 675, 730, and 850 nm. Radiometric correction images are taken of standard reflflectors on the ground before each flflight. The camera is set to take photos automatically, taking photos at an interval of a second, and the image is saved as TIFF format. The flflights were conducted in clear, cloudless and calm weather between 11 am and 1 pm.