Accurate fruit counting is one of the important phenotypic traits for crucial fruit harvesting decision making. Existing approaches perform counting through detection or regression counting. Detection of fruit instances is very challenging because of the very small fruit size as compared to the whole size image of a tree, while regression-based counting gives impressive results but becomes inaccurate when the number of instances increases. Moreover, most approaches lack scalability and are

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... cable only on one or two fruit types. In this paper, we propose a fruit counting mechanism that combines loose segmentation and regression counting that works on six fruit types, such as Apple, Orange, Tomato, Peach, Pomegranate and Almond. Through relaxed segmentation, fruit clusters are segmented to extract the small image regions which contain the small cluster of fruits. Extracted regions are forwarded for the regression counting of fruits. Relaxed segmentation is achieved through a state-of-the-art deconvolutional network, while modified Inception Residual Networks (ResNet) based nonlinear regression module is proposed for fruit counting. For segmentation, 4,820 original images, including corresponding mask images, of all six fruit types are augmented to 32,412 images through different augmentation techniques, while 21,450 extracted patches are augmented to 89,120 images used for the regression module training. The proposed approach has superseded the counting accuracy of existing approaches of individual fruit types, but we have achieved an overall 94.71% accuracy.