Sensor data is streamed to a stream explorer for classification. Classification data visualization allows drilling data.
Analogy - Google-Sanofi diabetes project: devices monitor insulin level and upload data to a cloud to manage insulin delivery.
Yuta Endo
Head of Product Marketing at FogHorn
Convolutional Neural Networks for Image Recognition in Agriculture
http://ceur-ws.org/Vol-1180/CLEF2014wn-Life-SunderhaufEt2014.pdf
1. Fine-Grained Plant Classification Using Convolutional Neural Networks for Feature Extraction
http://ceur-ws.org/Vol-1180/CLEF2014wn-Life-SunderhaufEt2014.pdf
Niko S¨underhauf, Chris McCool, Ben Upcroft, and Tristan Perez Agricultural Robotics Program, Queensland University of Technology 2 George Street, Brisbane QLD 4001, Australia http://www.tiny.cc/agrc-qut Corresponding author: niko.suenderhauf@qut.edu.au
Abstract. We present an overview of the QUT plant classification system submitted to LifeCLEF 2014. This system uses generic features extracted from a convolutional neural network previously used to perform general object classification. We examine the effectiveness of these features to perform plant classification when used in combination with an extremely randomised forest. Using this system, with minimal tuning, we obtained relatively good results with a score of 0.249 on the test set of LifeCLEF 2014.
Keywords: convolutional neural network, extremely random forest, plant classification
2. ImageCLEF 2015
http://www.imageclef.org/2015
3. LifeCLEF 2015 Plant task
http://www.imageclef.org/lifeclef/2015/plant
AI for Crop Yield Prediction
Artificial intelligence neural network yield precision agriculture
Google Scholar
https://scholar.google.com/scholar?q=Artificial+intelligence+neural+network+yield+precision+agriculture&hl=en&as_sdt=0&as_vis=1&oi=scholart&sa=X&ved=0CBsQgQMwAGoVChMImMuMvdTbyAIVQjeICh3SPQ1w
No comments:
Post a Comment