In order to improve the recognition accuracy and positioning accuracy of fruit picking robot, a target recognition and positioning algorithm based on deep learning Faster-RCNN framework was proposed. Firstly, the convolutional neural network VGG16 model was used to extract the characteristics information of the input image, and the region proposal network RPN was used to generate the candidate box containing the target. The adaptive number of candidate boxes was introduced to improve the performance of the algorithm. Then, the multi task loss function was used to classify the target and correct the prediction box. Finally, the mapping relationship between the two coordinate systems of the hand and eye of the picking robot was solved by calibration, so as to realize the accurate recognition and positioning of the fruit. The experimental results of apple recognition and location show that the proposed algorithm has high recognition accuracy, the average accuracy is 97.5%, and the location error is lower, the maximum error is only 1.33cm, which can provide strong technical support for the development of smart agriculture.