  This work presents a method for semantic segmentation of mango trees in high
resolution aerial imagery, and, a novel method for individual crown detection
of mango trees using segmentation output. Mango Tree Net, a fully convolutional
neural network (FCN), is trained using supervised learning to perform semantic
segmentation of mango trees in imagery acquired using an unmanned aerial
vehicle (UAV). The proposed network is retrained to separate
touching/overlapping tree crowns in segmentation output. Contour based
connected object detection is performed on the segmentation output from
retrained network. Bounding boxes are drawn on the original images using
coordinates of connected objects to achieve individual crown detection. The
training dataset consists of 8,824 image patches of size 240 x 240. The
approach is tested for performance on segmentation and individual crown
detection tasks using test datasets containing 36 and 4 images respectively.
The performance is analyzed using standard metrics precision, recall, f1-score
and accuracy. Results obtained demonstrate the robustness of the proposed
methods despite variations in factors such as scale, occlusion, lighting
conditions and surrounding vegetation.
