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App developers in Uganda use TensorFlow to spot armyworm damage in maize

[2019.11.13, Wed 22:05] Fall armyworm, the larval life stage of a Fall armyworm moth, impacts maize crops worldwide but particularly in countries like Uganda, where agricultural businesses employ 70% of the population. The threat of devastating losses prompted developers participating in a Google Developer Group in Mbale to create an Android app - FlatButter - that identifies signs of Fall armyworm damage in maize crops. "The vast damage and yield losses in maize production, due to FAW, got the attention of global organizations, who are calling for innovators to help," wrote Hansu Mobile and Intelligent Innovations CEO Nsubuga Hassan, who led the team that developed the app. The app developers first collected data samples from nearby fields, using their smartphones to capture images. Using TensorFlow Converter, a tool that takes a TensorFlow model and generates a lightweight version, they integrated a trained image classifier into the aforementioned FlatButter app. Currently, the team's training data set numbers nearly 4,000 data samples, but they say it's growing as they continue to snap images of affected maize. Scientists at Queensland University used Google's TensorFlow machine learning framework to train an algorithm that can automatically detect sea cows in ocean images.
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