Fig. 1. The acquisition system based on a multispectral camera
(a): The composition of the acquisition system; (b): DJI Phantom; (c): ADC Micro
1: DJI Phantom, a quad-rotor UAV; 2: ADC Micro, a multispectral camera; 3: an SD card with 16G storage; 4: Computer

Fig. 2. The acquisition of multispectral images in fields
(a): Experimental site; (b): Sampling scheme

Fig. 3. The acquired multispectral images
(a): An image example (1); (b): An image example (2);(c): An image example (3); (d): An image example (4)

Fig. 4. The examples of the images with R, G and NIR components processed by PixelWrench2 x64
(a): The image with R component;(b): The image with G component;(c): The image with Near-Infrared (NIR) conponent

Fig. 5. The results of R, G and NIR components
(a): R, G and NIR components in local areas;(b): R, G and NIR components in entire areas

Fig. 6. The portable sugar meter, PAL-1, and its real measurement
(a): A portable sugar meter, PAL-1;(b): Measuring total sugar of grape juice by using PAL-1

Fig. 7. The changing relation between each component and date
(a): The changing relation between R component and date; (b): The changing relation between G component and date;(c): The changing relation between NIR component and date

Fig. 8. The regression modelling between the local R component of the multispectral images and total sugar
(a): The linear model between the local R component of the multispectral images and total sugar;(b): The logarithmic model between the local R component of the multispectral images and total sugar

Table 1. The component results of local and entire areas

Table 2. The total sugar of grape juice of the model set

Table 3. The regression of each colour component

Table 4. The results of the regression analysis between the local R component of the multispectral images and total sugar

Table 5. The comparison between the total sugar of the validation set and that of the model prediction results