Abstract: Abstract: To realize rapid acquisition of massive phenotypic information of wheat breeding material, the studies on UAV (unmanned aerial vehicle) platform selection, sensor integration, and remote sensing data processing and analyses were carried out respectively, and a set of multi-load agricultural UAV based remote sensing system for assisting crop breeding information acquisition was developed. The speed and height of multi-rotor UAV could be controlled easily, even at low altitude. Different kinds of sensors such as digital camera, multi-spectral camera and infrared thermal imager, could be loaded on the UAV at the same time. The above characteristics make multi-rotor UAV most suitable to acquire different kinds of farmland spatial information at different scales readily. The crop lodging area could be obtained according to the generated ortho-image based on the high-definition digital images. The crop growth status and crop coverage could be estimated through multispectral images. The canopy temperature, as an important index related to crop growth, could be rapidly acquired by a thermal infrared sensor. The developed system was used in the breeding experiments in the breeding base of Academy of Agricultural Sciences in Lixiahe, Jiangsu from March to June in 2014. In order to acquire orthophotos, canopy spectrums and temperature of wheat breeding area, the Canon PowerShot G16 digital camera, the Tetracam ADC Lite multi spectral camera and the Optris PI thermal imager were loaded on the multi-rotor UAV. The position and altitude parameters of UAV were acquired by GPS (global positioning system) and IMU (inertial measurement unit) sensor simultaneously. The ASD FieldSpec Pro spectrometer collected reflectance data of black and white calibration cloth, cement and water synchronously. The data were subsequently used in multispectral camera radiometric calibration. The data were also tested by the FLIR SC620 thermal imager and the SPOT THERMOMETER HT-11D infrared radiometer for validating the acquired temperature data. At the same time, leaf area index (LAI) data of each plot were collected, and their accurate positions were recorded by hand-held differential GPS (centimeter level). The boundary extraction of breeding subarea and the lodging area estimation were conducted by the image recognition and artificial discrimination in the study. The lodging area estimated was up to 49.88 m2. Multispectral reflectance images were generated by strict radiometric calibration after accurate geometric correction. Vegetation indices, such as normalized difference vegetation index (NDVI), optimization soil adjusted vegetation index (OSAVI) and nitrogen reflectance index (NRI), were computed, respectively. The results showed that NDVI had a strong correlation with LAI (R2=0.48, RMSE=0.27, n=8). The wheat yield forecast was carried out by the nitrogen fertilization optimization algorithm in which INSEY (in-season estimate of yield) index was calculated by the NDVI and local weather data. The yield prediction model was established (R2=0.722, RMSE=0.45, n=25). In this study, the temperature of sky and ground observed were used in the thermal imager temperature correction. Quick mosaic was done to temperature image after correction and then canopy temperature data were extracted. Wheat canopy temperature acquired synchronously by infrared radiometer was combined in the validation with the accuracy (R2=0.84 and RMSE=1.77, n=14).