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Sensors 2017, 17(4), 905;

Deep Count: Fruit Counting Based on Deep Simulated Learning

Department of Computer Science, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 18 February 2017 / Revised: 5 April 2017 / Accepted: 7 April 2017 / Published: 20 April 2017
(This article belongs to the Section Physical Sensors)
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Recent years have witnessed significant advancement in computer vision research based on deep learning. Success of these tasks largely depends on the availability of a large amount of training samples. Labeling the training samples is an expensive process. In this paper, we present a simulated deep convolutional neural network for yield estimation. Knowing the exact number of fruits, flowers, and trees helps farmers to make better decisions on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits or flowers by workers is a very time consuming and expensive process and it is not practical for big fields. Automatic yield estimation based on robotic agriculture provides a viable solution in this regard. Our network is trained entirely on synthetic data and tested on real data. To capture features on multiple scales, we used a modified version of the Inception-ResNet architecture. Our algorithm counts efficiently even if fruits are under shadow, occluded by foliage, branches, or if there is some degree of overlap amongst fruits. Experimental results show a 91% average test accuracy on real images and 93% on synthetic images. View Full-Text
Keywords: deep learning; agricultural sensors; simulated learning; yield estimation deep learning; agricultural sensors; simulated learning; yield estimation

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Rahnemoonfar, M.; Sheppard, C. Deep Count: Fruit Counting Based on Deep Simulated Learning. Sensors 2017, 17, 905.

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