Ius and (see also Appendix A). Figure 3 shows the picture of
Ius and (see also Appendix A). Figure three shows the picture of an A). process described in Section 2 (see also Appendix olive tree extracted from the UAV orthophoto Figure 3 segmented with the kNN extracted in the UAV orthophoto (Fig(Figure 3a),shows the image of an olive treealgorithm (Figure 3b) and its canopy circumference ure 3a), segmented together with the kNN algorithm extracted using the algorithm described in Section two. (Figure 3c) provided the canopy radius(Figure 3b) and its canopy circumference (Figure 3c) given the canopy radius extracted with the algorithm described in Section 2.(a)(b)(c)Figure (a) Image of your Figure3.3. (a) Image ofolive tree ahead of image segmentation; (b) Image segmented with kNN the olive tree just before image segmentation; (b) Image segmented with kNN supervised studying algorithm; (c) Calculated canopy circumference PF-06873600 custom synthesis obtaining radius R. The patches supervised learning algorithm; (c)algorithm are marked in red. assigned towards the class “leaves” by the kNN Calculated canopy circumference obtaining radius R. The patchesassigned to the class “leaves” by the kNN algorithm are marked in red.To give an estimate from the olive regional productivity each the leaf area along with the canopy radius assessed in the UAV orthophoto reconstruction may be employed. Even so, for To give an estimate on the olive regional productivity both the leaf region plus the canopy all of the 4 regions considered it was found that the normalized leaf region is quadratically radius assessed from the UAV orthophoto reconstruction could be utilized. Nonetheless, for all correlated using the canopy radius. In certain, the regression equation holds, exactly where the 4 regions regarded as it and x discovered thatalready defined above. The re- is quadratically NLA stands for normalized leaf region was = R/Rmax was the normalized leaf region gression coefficients m canopy radius. In particular, 4 regions analysed. correlated with all the and q are reported in Table three for the the regression equation holds, where NLA = 2 +Table three. Regression coefficients of Equation (five).(five)RegionRegionRegionRegionDrones 2021, 5,9 ofstands for normalized leaf area and x = R/Rmax was already defined above. The regression coefficients m and q are reported in Table 3 for the four regions analysed. NLA = mx2 + q (five)Given these results, in principle it truly is irrelevant which variable is chosen for describing the system (leaf region or x = R/Rmax ). However, the overall kNN pixel classifier accuracy is 71.3 and pixel misclassification can happen. Conversely, very handful of pixels are required to draw the canopy circumference. As a result, although leaf region estimation for the individual tree may be inaccurate, the canopy boundary is detected really effectively and consequently the normalized canopy radius was regarded as an independent variable. Moreover, the canopy radius is usually straight measured in-field and may be utilised each as an external test for the model and as an input for the production estimate protocol. Note that the estimated leaf region was not reported due to the fact it was not C2 Ceramide Biological Activity employed for estimating the olive production. The principle result of Equation (5) is indeed that the leaf area is proportional to the square in the canopy radius. This justifies the use of the canopy radius (which can be less difficult to measure with respect for the leaf location) for estimating the olive production. 1st of all, for every single region among the 3 selected as education for the 10 of 16 the model, Drones 2021, 5, x FOR PEER Assessment productivity as a function of your normalized canopy ra.
