Summary of Convolutional Neural Network Ensemble Learning For Hyperspectral Imaging-based Blackberry Fruit Ripeness Detection in Uncontrolled Farm Environment, by Chollette C. Olisah et al.
Convolutional Neural Network Ensemble Learning for Hyperspectral Imaging-based Blackberry Fruit Ripeness Detection in Uncontrolled Farm Environment
by Chollette C. Olisah, Ben Trewhella, Bo Li, Melvyn L. Smith, Benjamin Winstone, E. Charles Whitfield, Felicidad Fernández Fernández, Harriet Duncalfe
First submitted to arxiv on: 9 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed multi-input convolutional neural network (CNN) ensemble classifier uses a pre-trained visual geometry group 16-layer deep convolutional network (VGG16) model trained on the ImageNet dataset. The fully connected layers are optimized for learning traits of ripeness in mature blackberry fruits, which lack visible ripeness cues. The input to the network is images acquired with a stereo sensor using visible and near-infrared (VIS-NIR) spectral filters at wavelengths of 700 nm and 770 nm. The model achieved 95.1% accuracy on unseen sets and 90.2% accuracy in field conditions. This study demonstrates the feasibility of machine sensory in detecting subtle traits of ripeness in blackberry fruits, with a high positive correlation to human sensory over skin texture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to tell when blackberries are ripe. Usually, you can’t tell just by looking at them because they stay black even when they’re ripe. To solve this problem, the authors created a special kind of artificial intelligence called a convolutional neural network (CNN). This AI uses pictures taken with a special camera that sees different kinds of light to figure out if a blackberry is ripe or not. The researchers tested their method and found it was correct about 95% of the time, even when the pictures were taken in real-world conditions. This means we can use machines to help us pick blackberries at just the right moment. |
Keywords
* Artificial intelligence * Cnn * Convolutional network * Neural network
