Some vegetative properties measured in fruit trees are important indicators in examining of plant growth calculation, estimation of leaf area index in evapotranspiration, fertilizer requirement etc. These measurements reflect the effects of the cultivation treatments in many areas of commercial growing and scientific studies. One of the most important measurements is the status of the canopy development. Canopy width, area and volume can be measured with some calculations. However, more technological equipment may be needed to reduce work and labor, and to make the results more precise and clearer. Recently, unmanned aerial vehicles, which have become widespread, have a wide potential for use in agriculture. By using image processing methods, it is possible to make more objective and high accuracy evaluations much faster. In this study, the images of the apple trees (Malus domestica Borkh) cultivar Golden grafted onto MM106 rootstock, were taken by light unmanned aerial vehicle to calculate the canopy area and then these images were analyzed using image processing methods for calculating canopy areas. Both circular and elliptical calculation methods were used. The area calculations with image processing methods were compared with the areas obtained manually. Comparisons were made by regression analysis. For the most successful method R value was 0.9662 for elliptic area and 0.9346 for circular area which was calculated by image processing. The results demonstrated that the image processing can be an alternative method to determine the canopy area according to accuracy ratios.
Design and construction of a prototype of an unmanned aerial vehicle equipped with artificial vision for the search of people
