Fruit detection and counting issue is of great importance in the sector of agriculture. At present mostly counting is done by the farmers manually. It consumes a lot of time and also need lot of labors. So, it needs to be done automatically. Recently, deep neural network has been extensively studied in the detection of fruits for automatic detection and counting of the fruits in the field. The purpose of this paper is an extensive review of existing literature and deep learning models/techniques available for fruit detection and counting. A review of development in the deep learning models is presented.

KEYWORDS:  Fruit Detection, Mask R-CNN, Faster R-CNN, YOLO, Deep Learning.