Image classification using Deep Learning, to solve problems in the agricultural domain

Project by Adam

Abstract

The project goal was to create a classifier that can distinguish between tree images and non-tree images (like bushes/grass/overlapping trees). A deep neural network was trained over a hand curated dataset and results were fair. In the second round of learning, lessons were employed and a new dataset was curated at even higher quality. The model hyper-parameters were also adjusted and the results were fantastic! 0.98 accuracy! 

Challenges

  • Data was dirty making it impossible to run off-the-bat.
  • Data collection from multiple sources.
  • Investigating why the network doesn’t work, how to test it, how to improve the quality of the data.

Achievements (according to KPIs)

  • A classifier model was trained and tested. Accuracy on a balanced binary dataset reached 0.98!
  • Visualization and statistics were produced using the model’s output to gain insights on where the model works and where it needs to improve.

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