## 4 Results
### Stoneville, Mississippi
With UAV image data, 3D crop canopy models over the field can be built to recognize and count the yield components, such as panicle and spikelet, using the recognition, classification, and analysis of pattern recognition algorithms. With the recognition and counting data, a special spikelet index can be quantified to monitor the spikelet number of the crop in the flowering stage for yield. Within this aspect, a study was conducted in a cotton field of 65K m\({}^{2}\) in the research farm area at Stoneville, Mississippi to segment cotton bolls from the background of the UAV digital images of the defoliated cotton field just prior to harvest. Cotton yield was estimated with the estimated cotton plot unit coverage as the image metric, defined as a ratio for a cotton unit coverage (CUC) in each field plot, as shown in [41]:  
\[\text{CUC}=\frac{NoCP}{NoPP}\]  
Figure 3: Orthomosaic thermal image (25 May 2017) of the research farm area at Stoneville, Mississippi. where \(NoCp\) is the number of cotton boll pixels in an experimental plot unit and \(NoPP\) is the number of pixels in the plot unit area. The ratio can be calculated using the number of cotton boll pixels in a field plot divided by the number of pixels in the plot area. In this study, it was initially assumed that cotton bolls are globally brighter (white) and the method of RGB thresholding was directly used for image processing, but the correlation between the yield and the CUC was low (0.3), which might be because of the background interference of soil, defoliated cotton leaves and dead cotton stems/branches, and occluded cotton bolls. Therefore, cotton bolls were further assumed, as they are only locally brighter. Based on this assumption, Laplacian of the RGB images was conducted, and this increased the correlation (to 0.58) because this only worked for small bolls, while large bolls missed the fill-up in the enclosures because Laplacian of images is basically for edge detection (Figure 4). Then, thresholding of Laplacian of Gaussian (LoG) of the UAV images was applied to completely segment out the cotton bolls and boll clusters to accurately quantify the CUC for cotton yield estimation with relatively high correlation (0.86) (Figure 5). Figure 6 shows the multispectral imagery of Landsat 8, Sentinel 2A, and PlanetScope over the Stoneville, Mississippi research area in September 2016. In Figure 2, the relatively high resolution of the manned aircraft images can clearly delineate the boundaries of the fields, and even differentiate the variation within the fields with. However, in Figure 6, the satellite images all have coarser resolutions compared with airborne images, but each of them still has its own function for the area of remote sensing. To cover this area of crop fields, Landsat 8 imagery can roughly delineate the boundaries of the sections of the fields to differentiate types of vegetation (crops and weeds) over the area. Sentinel imagery can delineate the boundaries of the fields for improved differentiation of vegetation types. PlanetScope imagery can clearly delineate the boundaries of the fields but is limited to differentiate the within-field variations, which can be overcome by airborne platforms, manned aircraft, and UAVs. Figure 4: CUC quantification (**a**) LoG of UAV RGB images of individual small cotton boll; (**b**) LoG of UAV RGB images of large cotton boll/boll clusters; (**c**) thresholding of LoG of UAV RGB images of large cotton boll/boll clusters.