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Aerobotics improves training speed by 24 times per sample with Amazon SageMaker and TensorFlow

 Aerobotics improves training speed by 24 times per sample with Amazon SageMaker and TensorFlow

Editor’s note: This is a guest post written by Michael Malahe, Head of Data at Aerobotics, a South African startup that builds AI-driven tools for agriculture.

Aerobotics is an agri-tech company operating in 18 countries around the world, based out of Cape Town, South Africa. Our mission is to provide intelligent tools to feed the world. We aim to achieve this by providing farmers with actionable data and insights on our platform, Aeroview, so that they can make the necessary interventions at the right time in the growing season. Our predominant data source is aerial drone imagery: capturing visual and multispectral images of trees and fruit in an orchard.

In this post we look at how we use Amazon SageMaker and TensorFlow to improve our Tree Insights product, which provides per-tree measurements of important quantities like canopy area and health, and provides the locations of dead and missing trees. Farmers use this information to make precise interventions like fixing irrigation lines, applying fertilizers at variable rates, and ordering replacement trees. The following is an image of the tool that farmers use to understand the health of their trees and make some of these decisions.

To provide this information to make these decisions, we first must accurately assign each foreground pixel to a single unique tree. For this instance segmentation task, it’s important that we’re as accurate as possible, so we use a machine learning (ML) model that’s been effective in large-scale benchmarks. The model is a variant of Mask R-CNN, which pairs a convolutional neural network (CNN) for feature extraction with several additional components for detection, classification, and segmentation. In the following image, we show some typical outputs, where the pixels belong to a given tree are outlined by a contour.

Glancing at the outputs, you might think that the problem is solved.

The challenge

The main challenge with analyzing and modeling agricultural data is that it’s highly varied across a number of dimensions.

The following image illustrates some extremes of the variation in the size of trees and the extent to which they can be unambiguously separated.

In the grove of pecan trees, we have one of the largest trees in our database, with an area of 654 m2 (a little over a minute to walk around at a typical speed). The vines to the right of the grove measure 50 cm across (the size of a typical potted plant). Our models need to be tolerant to these variations to provide accurate segmentations regardless of the scale.

An additional challenge is that the sources of variation aren’t static. Farmers are highly innovative, and best practices can change significantly over time. One example is ultra-high-density planting for apples, where trees are planted


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