Zhejiang University

Han Guo

Jun Zhou

Fei Liu

Wenwen Kong

Jiangang Shen

Yong He

Hui Fang

Yufei Liu

Lei Feng

Disclosed a crop yield prediction method and system based on low-altitude remote sensing information from an unmanned aerial vehicle (UAV). Obtaining a plurality of images taken by the UAV; stitching the plurality of images to obtain a stitched image; performing spectral calibration on the stitched image to obtain the reflectivity of each pixel in the stitched image; using a threshold segmentation method to segment the stitched image, to obtain a target area for crop yield prediction; using a Pearson correlation analysis method to analyze a correlation between the reflectivity of each band and the growth status and yield of the crop to obtain feature bands; constructing yield prediction factors based on the feature bands; and determining a predicted crop yield value of the target area for crop yield prediction based on the yield prediction factors and a crop planting area of the target area for crop yield prediction.

Crop yield prediction method and system based on low-altitude remote sensing information from unmanned aerial vehicle

The invention relates to a method and device for predicting a volume median diameter (VMD) in an overlapped spray area of twin nozzles. Placing a single nozzle at different heights to determine a spray area; measuring all VMDs in the spray area to obtain first true measured values; dividing the first true measured values to construct a first calibration set and a first prediction set; establishing a polynomial fitting formula by a REGRESS function; placing twin nozzles at different heights and different nozzle spacing, and measuring VMDs in the overlapped spray area to obtain second true measured values; determining simulated values of the VMDs of first and second nozzles; dividing the simulated values of the VMDs and the second true measured values to construct a second calibration set and a second prediction set; quantitatively calibrating by using a radial basis function neural network (RBFNN) to obtain a prediction model; determining a VMD in the overlapped spray area of twin nozzles.

Method and device for predicting volume median diameter in overlapped spray area of twin nozzles

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Hangzhou, China

Han Guo