Fruit Detection System Using Deep learning

Description

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In this project we have implemented fruit detection system for automatic harvesting of fruits .The system was implemented to detect the matured strawberry fruits from the plants , so that the robots could plug the fruits from the plants and collect on the tray. This image processing sytem used matlab for image processing.We have used deep neural networks for training .The proposed algorithm works well and compared with other algorithms in terms of accuracy.

Block Diagram of Fruit detection system using Deep Learning

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The proposed method consist of four modules

1) Base network image classifier A set of convolutional layers for basic image classification.

2) Multi-scale feature maps for detection Additional convolutional feature layers of progressively decreasing size which augment the truncated base network to allow predictions of detections at multiple scales.

3) Convolutional predictors for detection A set of convolutional feature layers which generate a fixed set of predictions using a set of convolutional filters.

4) Default boxes and aspect ratios Layers that generate and associate default bounding boxes with feature map cells and compute per-class scores for each identified bounding box.

Tool used ๐Ÿ˜• Matlab 2018 b

Toolbox required : Deep Learning Toolbox

Conclusion

Our system strives to be fast, precise, and affordable to spur easy adoption and high efficiency. We implement the state-of-the-art SSD neural network framework as a fruit detector. We use a sparse, three layer convolutional classifier as our base classifier, and alter the network in various ways to increase speed and precision.

Reference Paper

[1] A Strawberry Detection System Using Convolutional Neural Networks

?Published in:?2018 IEEE International Conference on Big Data (Big Data)

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