A CNN-Based Method for Fruit Detection in Apple Tree Images

The paper proposes a method for fruit detection in images of apple trees in industrial orchards, based on the use of a two-stage neural network designed for the localization of objects. The neural network generates a segmentation mask and a bounding box for each fruit found on the input image. This information can then be used to count the number of fruits in the images and to construct yield estimate. It is shown that by training the neural network with the Adam optimizer and a smaller learning rate, the quality of fruit detection of all sizes, and especially of small fruits, can be improved.