In Computer vision, object detection has become one of the most popular research fields. Human eyes can distinguish a various number of objects in images with less exertion, despite the fact that the objects in the images differ at various perspectives. This task is as yet a challenge for Computer vision frameworks. The goal is to present an efficient approach for fruit detection which can be used for yield estimation. In this paper, an efficient survey presented for Fruit detection in order to estimate the yield using Convolutional Neural Networks. This approach mainly benefits farmers and also applying automation in the field of agriculture, this helped to create several advancements to the industry. Various methods are surveyed in this paper in order to solve the existing problems of Fruit detection.

Convolutional Neural networks, fruit detection, learning methods

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