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As global food demand continues to rise and myriad threats of climate change intensify, creating more sustainable agricultural practices has become increasingly essential. This is especially true in remote areas of the world, where advanced agricultural expertise is scarce and smallholder farmers with limited resources (both financial and material) cultivate an estimated 80% of farmland.

Continue reading “PlantVillage: Helping farmers in East Africa detect and treat plant disease”

In this article, I’ll show you how to build your own real-time object detection iOS app. Thanks to other people’s articles, you can easily train your own object recognition model using TensorFlow’s Object Detection API and integrate the trained model into your iOS app.

Continue reading “Building a Real-time Object Detection iOS App That Detects Sushi”

As a mobile developer looking to integrate computer vision-based machine learning in your app(s), adding camera functionality is one of the most crucial aspects of the entire process.

You not only need the library to be stable and lightweight, but that library should also support the vast array of Android devices out there, most of which have slightly different camera implementations.

Continue reading “CameraX: _The_ Machine Learning Camera Library for Android”

This blog is the fourth one in my series on training and running Tensorflow models in a Python environment. If you haven’t read my earlier blogs centered on AutoML and machine learning on edge devices, I’d suggest that you do so before continuing with this post.

Continue reading “Using Google Cloud AutoML Edge Object Detection Models in Python”

This is the ninth post in my series around training and running Cloud AutoML models on the edge. This post follows up from the post earlier on training a multi-label image classification model and covers how to run the trained model in a python environment.

Continue reading “Using Google Cloud AutoML Multi-Label Image Classification Models in Python”

In recent years, neural networks and deep learning have sparked tremendous progress in the field of natural language processing (NLP) and computer vision.

While many of the face, object, landmark, logo, and text recognition and detection technologies are provided for Internet-connected devices, we believe that the ever-increasing computational power of mobile devices can enable the delivery of these technologies into the hands of users anytime, anywhere, regardless of Internet connection.

Continue reading “Comparing MobileNet Models in TensorFlow”

This post is a part of a series about feature engineering techniques for machine learning with Python.

Hey again! In this post, we’re going to explore feature scaling transformations for feature engineering.

Let’s start with feature magnitude. Frequently, our dataset contains features that highly vary in scales, and that’s a problem for some algorithms that rely on distance.

Continue reading “Hands-on with Feature Engineering Techniques: Feature Scaling”

In the previous article of this series on developing Flutter applications with TensorFlow Lite, we looked at how we can develop digit recognizer with Flutter and TensorFlow Lite and image classification with Flutter and TensorFlow Lite.

In the third article of this series, we’ll keep working with TensorFlow Lite, this time focusing on implementing object detection. The application we are going to build will be able to recognize objects presented in an image.

Continue reading “Object Detection with Flutter and TensorFlow Lite”