Lesson 15 (#54)

* Adding content

* Update en.json

* Update README.md

* Adding structure for project 4

* More on lesson 15

* Update TRANSLATIONS.md

* Adding lesson tempolates

* Adding more AI stuff

* Fixing code files with each others code in

* Update README.md

* Update virtual-device.md

* Bananas!

![](https://media.giphy.com/media/1uPiL9Amv5zkk/giphy.gif)

* Adding assignment

* Tweaks

.vscode/settings.json

@@ -0,0 +1,20 @@
+{
+    "cSpell.words": [
+        "ADCs",
+        "Geospatial",
+        "Kbps",
+        "Mbps",
+        "Seeed",
+        "Twilio",
+        "UART",
+        "UDID",
+        "Zigbee",
+        "antimeridian",
+        "geofence",
+        "geofences",
+        "geofencing",
+        "microcontrollers",
+        "mosquitto",
+        "sketchnote"
+    ]
+}

1-getting-started/lessons/1-introduction-to-iot/virtual-device.md

@@ -34,7 +34,7 @@ One of the powerful features of Python is the ability to install [pip packages](
 
 By default when you install a package it is available everywhere on your computer, and this can lead to problems with package versions - such as one application depending on one version of a package that breaks when you install a new version for a different application. To work around this problem, you can use a [Python virtual environment](https://docs.python.org/3/library/venv.html), essentially a copy of Python in a dedicated folder, and when you install pip packages they get installed just to that folder.
 
-#### Task
+#### Task - configure a Python virtual environment
 
 Configure a Python virtual environment and install the pip packages for CounterFit.
 
@@ -96,7 +96,7 @@ Configure a Python virtual environment and install the pip packages for CounterF
 
 Once the Python virtual environment is ready, you can write the code for the 'Hello World' application
 
-#### Task
+#### Task - write the code
 
 Create a Python application to print `"Hello World"` to the console.
 
@@ -119,7 +119,8 @@ Create a Python application to print `"Hello World"` to the console.
     ```sh
     code .
     ```
-    > 💁 If your terminal returns `command not found` on macOS it means VS Code has not been added to PATH, you can [add VS Code to PATH](https://code.visualstudio.com/docs/setup/mac#_launching-from-the-command-line) and run the command afterwards. VS Code is installed to PATH by default on Windows and Linux.
+
+    > 💁 If your terminal returns `command not found` on macOS it means VS Code has not been added to PATH, you can add VS Code to PATH by following the instructions in the [Launching from the command line section of the VS Code documentation](https://code.visualstudio.com/docs/setup/mac?WT.mc_id=academic-17441-jabenn#_launching-from-the-command-line) and run the command afterwards. VS Code is installed to PATH by default on Windows and Linux.
 
 1. When VS Code launches, it will activate the Python virtual environment. You will see this in the bottom status bar:
 
@@ -171,7 +172,7 @@ Create a Python application to print `"Hello World"` to the console.
 
 As a second 'Hello World' step, you will run the CounterFit app and connect your code to it. This is the virtual equivalent of plugging in some IoT hardware to a dev kit.
 
-#### Task
+#### Task - connect the 'hardware'
 
 1. From the VS Code terminal, launch the CounterFit app with the following command:
 

3-transport/README.md

@@ -10,7 +10,7 @@ IoT can help with this supply chain by tracking the food in transit - ensuring d
 
 In these 4 lessons, you'll learn how to apply the Internet of Things to improve the supply chain by monitoring food as it is loaded onto a (virtual) truck, which is tracked as it moves to it's destination. You will learn about GPS tracking, how to store and visualize GPS data, and how to be alerted when a truck arrives at its destination.
 
-> 💁 These lessons will use some cloud resources. If you don't complete all the lessons in this project, make sure you follow the [Clean up your project](lessons/4-keep-your-plant-secure/README.md#clean-up-your-project) step in [lesson 4](lessons/6-keep-your-plant-secure/README.md).
+> 💁 These lessons will use some cloud resources. If you don't complete all the lessons in this project, make sure you [Clean up your project](../clean-up.md).
 
 ## Topics
 

4-manufacturing/README.md

@@ -0,0 +1,24 @@
+# Manufacturing and processing - using IoT to improve the processing of food
+
+Once food reaches a central hub or processing plant, it isn't always just shipped out to supermarkets. A lot of the time the food goes through a number of processing steps, such as sorting by quality. This is a process that used to be manual - it would start in the field when pickers would only pick ripe fruit, then at the factory the fruit would be ride a conveyer belt and employees would manually remove any bruised or rotten fruit. Having picked and sorted strawberries myself as a summer job during school, I can testify that this isn't a fun job.
+
+More modern setups rely on IoT for sorting. Some of the earliest devices like the sorters from [Weco](https://wecotek.com) use optical sensors to detect the quality of produce, rejecting green tomatoes for example. These can be deployed in harvesters on the farm itself, or in processing plants.
+
+As advances happen in Artificial Intelligence (AI) and Machine Learning (ML), these machines can become more advanced, using ML models trained to distinguish between fruit and foreign objects such as rocks, dirt or insects. These models can also be trained to detect fruit quality, not just bruised fruit but early detection of disease or other crop problems.
+
+> 🎓 The term *ML model* refers to the output of training machine learning software on a set of data. For example, you can train a ML model to distinguish between ripe and unripe tomatoes, then use the model on new images to see if the tomatoes are ripe or not.
+
+In these 4 lessons you'll learn how to train image-based AI models to detect fruit quality, how to use these from an IoT device, and how to run these on the edge - that is on an IoT device rather than in the cloud.
+
+> 💁 These lessons will use some cloud resources. If you don't complete all the lessons in this project, make sure you [Clean up your project](../clean-up.md).
+
+## Topics
+
+1. [Train a fruit quality detector](./4-manufacturing/lessons/1-train-fruit-detector/README.md)
+1. [Check fruit quality from an IoT device](./4-manufacturing/lessons/2-check-fruit-from-device/README.md)
+1. [Run your fruit detector on the edge](./4-manufacturing/lessons/3-run-fruit-detector-edge/README.md)
+1. [Trigger fruit quality detection from a sensor](./4-manufacturing/lessons/4-trigger-fruit-detector/README.md)
+
+## Credits
+
+All the lessons were written with ♥️ by [Jim Bennett](https://GitHub.com/JimBobBennett)

4-manufacturing/lessons/1-train-fruit-detector/README.md

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+# Train a fruit quality detector
+
+Add a sketchnote if possible/appropriate
+
+This video gives an overview of the Azure Custom Vision service, a service that will be covered in this lesson.
+
+[![Custom Vision – Machine Learning Made Easy | The Xamarin Show](https://img.youtube.com/vi/TETcDLJlWR4/0.jpg)](https://www.youtube.com/watch?v=TETcDLJlWR4)
+
+## Pre-lecture quiz
+
+[Pre-lecture quiz](https://brave-island-0b7c7f50f.azurestaticapps.net/quiz/29)
+
+## Introduction
+
+The recent rise in Artificial Intelligence (AI) and Machine Learning (ML) is providing a wide range of capabilities to todays developers. ML models can be trained to recognize different things in images, including unripe fruit, and this can be used in IoT devices to help sort produce either as it is being harvested, or during processing in factories or warehouses.
+
+In this lesson you will learn about image classification - using ML models to distinguish between images of different things. You will learn how to train an image classifier to distinguish between fruit that is good, and fruit that is bad, either under or over ripe, bruised, or rotten.
+
+In this lesson we'll cover:
+
+* [Using AI and ML to sort food](#using-ai-and-ml-to-sort-food)
+* [Image classification via Machine Learning](#image-classification-via-machine-learning)
+* [Train an image classifier](#train-an-image-classifier)
+* [Test your image classifier](#test-your-image-classifier)
+
+## Using AI and ML to sort food
+
+Feeding the global population is hard, especially at a price that makes food affordable for all. One of the largest costs is labor, so farmers are increasingly turning to automation and tools like IoT to reduce their labor costs. Harvesting by hand is labor intensive (and often backbreaking work), and is being replaced by machinery, especially in richer nations. Despite the savings in cost of using machinery to harvest, there is a downside - the ability to sort food as it is being harvested.
+
+Not all crops ripen evenly. Tomatoes, for example, can still have some green fruits on the vine when the majority is ready for harvest. Although it is a waste to harvest these early, it is cheaper and easier for the farmer to harvest everything using machinery and dispose of the unripe produce later.
+
+✅ Have a look at different fruits or vegetables, either growing near you in farms or in your garden, or in shops, Are they all the same ripeness, or do you see variation?
+
+The rise of automated harvesting moved the sorting of produce from the harvest to the factory. Food would travel on long conveyer belts with teams of people picking over the produce removing anything that wasn't up to the required quality standard. Harvesting was cheaper thanks to machinery, but there was still a cost to manually sort food.
+
+![If a red tomato is detected it continues its journey uninterrupted. If a green tomato is detected it is flicked into a waste bin by a lever](../../../images/optical-tomato-sorting.png)
+
+***If a red tomato is detected it continues its journey uninterrupted. If a green tomato is detected it is flicked into a waste bin by a lever. tomato by parkjisun from the Noun Project - from the [Noun Project](https://thenounproject.com)***
+
+The next evolution was to use machines to sort, either built into the harvester, or in the processing plants. The first generation of these machines used optical sensors to detect colors, controlling actuators to push green tomatoes into a waste bin using levers or puffs of air, leaving red tomatoes to continue on a network of conveyor belts.
+
+The video below shows one of these machines in action.
+
+[![Automatic sorting of tomatoes via color](https://img.youtube.com/vi/AcRL91DouAU/0.jpg)](https://www.youtube.com/watch?v=AcRL91DouAU)
+
+In this video, as tomatoes fall from one conveyer belt to another, green tomatoes are detected and flicked into a bin using levers.
+
+✅ What conditions would you need in a factory or in a field for these optical sensors to work correctly?
+
+The latest evolutions of these sorting machines take advantage of AI and ML, using models trained to distinguish good produce from bad, not just by obvious color differences such as green tomatoes vs red, but by more subtle differences in appearance that can indicate disease or bruising.
+
+## Image classification via Machine Learning
+
+Traditional programming is where you take data, apply an algorithm to the data, and get output. For example, in the last project you took GPS coordinates and a geofence, applied an algorithm that was provided by Azure Maps, and got back a result of if the point was inside or outside the geofence. You input more data, you get more output.
+
+![Traditional development takes input and an algorithm and gives output. Machine learning uses input and output data to train a model, and this model can take new input data to generate new output](../../../images/traditional-vs-ml.png)
+
+Machine learning turns this around - you start with data and known outputs, and the machine learning tools work out the algorithm. You can then take that algorithm, called a *machine learning model*, and input new data and get new output.
+
+> 🎓 The process of a machine learning tool generating a model is called *training*. The inputs and known outputs are called *training data*.
+
+For example, you could give a model millions of pictures of unripe bananas as input training data, with the training output set as `unripe`, and millions of ripe banana pictures as training data with the output set as `ripe`. The ML tools will then generate a model. You then give this model a new picture of a banana and it will predict if the new picture is a ripe or an unripe banana.
+
+> 🎓 The results of ML models are called *predictions*
+
+![2 bananas, a ripe one with a prediction of 99.7% ripe, 0.3% unripe, and an unripe one with a prediction of 1.4% ripe, 98.6% unripe](../../../images/bananas-ripe-vs-unripe-predictions.png)
+
+ML models don't give a binary answer, instead they give probabilities. For example, a model may be given a picture of a banana and predict `ripe` at 99.7% and `unripe` at 0.3%. Your code would then pick the best prediction and decide the banana is ripe.
+
+The ML model used to detect images like this is called an *image classifier* - it is given labelled images, and then classifies new images based off these labels.
+
+## Train an image classifier
+
+To successfully train an image classifier you need millions of images. As it turns out, once you have an image classifier trained on millions or billions of assorted images, you can re-use it and re-train it using a small set of images and get great results, using a process called *transfer learning*.
+
+> 🎓 Transfer learning is where you transfer the learning from an existing ML model to a new model based off new data.
+
+Once an image classifier has been trained for a wide variety of images, it's internals are great at recognizing shapes, colors and patterns. Transfer learning allows the model to take what it has already learned in recognizing image parts, and use that to recognize new images.
+
+![Once you can recognize shapes, they can be put into different configurations to make a boat or a cat](../../../images/shapes-to-images.png)
+
+You can think of this as a bit like children's shape books, where once you can recognize a semi-circle, a rectangle and a triangle, you can recognize a sailboat or a cat depending on the configuration of these shapes. The image classifier can recognize the shapes, and the transfer learning teaches it what combination makes a boat or a cat - or a ripe banana.
+
+There are a wide range of tools that can help you do this, including cloud-based services that can help you train your model, then use it via web APIs.
+
+> 💁 Training these models takes a lot of computer power, usually via Graphics Processing Units, or GPUs. The same specialized hardware that makes games on your Xbox look amazing can also be used to train machine learning models. By using the cloud you can rent time on powerful computers with GPUs to train these models, getting access to the computing power you need, just for the time you need it.
+
+## Custom Vision
+
+Custom Vision is a cloud based tool for training image classifiers. It allows you to train a classifier using only a small number of images. You can upload images through a web portal, web API or an SDK, giving each image a *tag* that has the classification of that image. You then train the model, and test it out to see how well it performs. Once you are happy with the model, you can publish versions of it that can be accessed through a web API or an SDK.
+
+> 💁 You can train a custom vision model with as little as 5 images per classification, but more is better. You can get better results with at least 30 images.
+
+Custom Vision is part of a range of AI tools from Microsoft called Cognitive Services. These are AI tools that can be used either without any training, or with a small amount of training. They include speech recognition and translation, language understanding and image analysis. These are available with a free tier as services in Azure.
+
+> 💁 The free tier is more than enough to create a model, train it, then use it for development work. You can read about the limits of the free tier on the [Custom Vision Limits and quotas page on Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/limits-and-quotas?WT.mc_id=academic-17441-jabenn).
+
+### Task - create a cognitive services resource
+
+To use Custom Vision, you first need to create two cognitive services resources in Azure using the Azure CLI, one for Custom Vision training and one for Custom Vision prediction.
+
+1. Create a Resource Group for this project called `fruit-quality-detector`
+
+1. Use the following command to create a free Custom Vision training resource:
+
+    ```sh
+    az cognitiveservices account create --name fruit-quality-detector-training \
+                                        --resource-group fruit-quality-detector \
+                                        --kind CustomVision.Training \
+                                        --sku F0 \
+                                        --yes \
+                                        --location <location>
+    ```
+
+    Replace `<location>` with the location you used when creating the Resource Group.
+
+    This will create a Custom Vision training resource in your Resource Group. It will be called `fruit-quality-detector-training` and use the `F0` sku, which is the free tier. The `--yes` option means you agree to the terms and conditions of the cognitive services.
+
+1. Use the following command to create a free Custom Vision prediction resource:
+
+    ```sh
+    az cognitiveservices account create --name fruit-quality-detector-prediction \
+                                        --resource-group fruit-quality-detector \
+                                        --kind CustomVision.Prediction \
+                                        --sku F0 \
+                                        --yes \
+                                        --location <location>
+    ```
+
+    Replace `<location>` with the location you used when creating the Resource Group.
+
+    This will create a Custom Vision prediction resource in your Resource Group. It will be called `fruit-quality-detector-prediction` and use the `F0` sku, which is the free tier. The `--yes` option means you agree to the terms and conditions of the cognitive services.
+
+### Task - create an image classifier project
+
+1. Follow the [Create a new Project section of the Build a classifier quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier?WT.mc_id=academic-17441-jabenn#create-a-new-project) to create a new Custom Vision project. The UI may change and these docs are always the most up to date reference.
+
+    Call your project `fruit-quality-detector`.
+
+    When you create your project, make sure to use the `fruit-quality-detector-training` resource you created earlier. Use a *Classification* project type, a *Multiclass* classification type, and the *Food* domain.
+
+    ![The settings for the custom vision project with the name set to fruit-quality-detector, no description, the resource set to fruit-quality-detector-training, the project type set to classification, the classification types set to multi class and the domains set to food](../../../images/custom-vision-create-project.png)
+
+### Task - train your image classifier project
+
+To train an image classifier, you will need multiple pictures of fruit, both good and bad quality to tag as good and bad, such as an ripe and an overripe banana.
+
+> 💁 These classifiers can classify images of anything, so if you don't have fruit to hand of differing quality, you can use two different types of fruit, or cats and dogs!
+
+Ideally each picture should be just the fruit, with either a consistent background, or a wide variety of backgrounds. Ensure there's nothing in the background that is specific to ripe vs unripe fruit.
+
+> 💁 It's important not to have specific backgrounds, or specific items that are not related to the thing being classified for each tag, otherwise the classifier may just classify based on the background. There was a classifier for skin cancer that was trained on moles both normal and cancerous, and the cancerous ones all had rulers against them to measure the size. It turned out the classifier was almost 100% accurate at identifying rulers in pictures, not cancerous moles.
+
+1. Gather pictures for your classifier. You will need at least 5 pictures for each label to train the classifier, but the more the better. You will also need a few additional images to test the classifier. These images should all be different images of the same thing. For example:
+
+    * Using 2 ripe bananas, take some pictures of each one from a few different angles, taking at least 7 pictures (5 to train, 2 to test), but ideally more.
+
+        ![Photos of 2 different bananas](../../../images/banana-training-images.png)
+
+    * Repeat the same process using 2 unripe bananas
+
+    You should have at least 10 training images, with at least 5 ripe and 5 unripe, and 4 testing images, 2 ripe, 2 unripe. You're images should be png or jpegs, small than 6MB. If you create them with an iPhone for example they may be high-resolution HEIC images, so will need to be converted and possibly shrunk. The more images the better, and you should have a similar number of ripe and unripe.
+
+    If you don't have both ripe and unripe fruit, you can use different fruits, or any two objects you have available. You can also find some example images in the [images](./images) folder of ripe and unripe bananas that you can use.
+
+1. Follow the [Upload and tag images section of the Build a classifier quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier?WT.mc_id=academic-17441-jabenn#upload-and-tag-images) to upload your training images. Tag the ripe fruit as `ripe`, and the unripe fruit as `unripe`.
+
+    ![The upload dialogs showing the upload of ripe and unripe banana pictures](../../../images/image-upload-bananas.png)
+
+1. Follow the [Train the classifier section of the Build a classifier quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier?WT.mc_id=academic-17441-jabenn#train-the-classifier) to train the image classifier on your uploaded images.
+
+    You will be given a choice of training type. Select **Quick Training**.
+
+The classifier will then train. It will take a few minutes for the training to complete.
+
+> 🍌 If you decide to eat your fruit whilst the classifier is training, make sure you have enough images to test with first!
+
+## Test your image classifier
+
+Once your classifier is trained, you can test it by giving it a new image to classify.
+
+### Task - test your image classifier
+
+1. Follow the [Test and retrain a model with Custom Vision Service documentation on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/test-your-model?WT.mc_id=academic-17441-jabenn#test-your-model) to test your image classifier. Use the testing images you created earlier, not any of the images you used for training.
+
+    ![A unripe banana predicted as unripe with a 98.9% probability, ripe with a 1.1% probability](../../../images/banana-unripe-quick-test-prediction.png)
+
+1. Try all the testing images you have access to and observe the probabilities.
+
+---
+
+## 🚀 Challenge
+
+Image classifiers use machine learning to make predictions about what is in an image, based of probabilities that particular features of an image mean that it matches a particular label. It doesn't understand what is in the image - it doesn't know what a banana is or understand what makes a banana a banana instead of a boat.
+
+What do you think would happen if you used a picture of a strawberry with a model trained on bananas, or a picture of an inflatable banana, or a person in a banana suit, or even a yellow cartoon character like someone from the Simpsons?
+
+Try it out and see what the predictions are. You can find images to try with using [Bing Image search](https://www.bing.com/images/trending).
+
+## Post-lecture quiz
+
+[Post-lecture quiz](https://brave-island-0b7c7f50f.azurestaticapps.net/quiz/30)
+
+## Review & Self Study
+
+* When you trained your classifier, you would have seen values for *Precision*, *Recall*, and *AP* that rate the model that was created. Read up on what these values are using [the Evaluate the classifier section of the Build a classifier quickstart on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-build-a-classifier?WT.mc_id=academic-17441-jabenn#evaluate-the-classifier)
+* Read up on how to improve your classifier from the [How to improve your Custom Vision model on the Microsoft docs](https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/getting-started-improving-your-classifier?WT.mc_id=academic-17441-jabenn)
+
+## Assignment
+
+[Train your classifier for multiple fruits and vegetables](assignment.md)

4-manufacturing/lessons/1-train-fruit-detector/assignment.md

@@ -0,0 +1,16 @@
+# Train your classifier for multiple fruits and vegetables
+
+## Instructions
+
+In this lesson you trained an image classifier to be able to distinguish between ripe and unripe fruits, but only using one type of fruit. A classifier can be trained to recognize multiple fruits, with varying rates of success depending on the type of fruit and the difference between ripe and unripe.
+
+For example, with fruits that change color when they ripen, image classifiers might be less effective than a color sensor as they usually work on grey scale images instead of full color.
+
+Train your classifier with other fruits to see how well it works, especially when fruits look similar. For example, apples and tomatoes.
+
+## Rubric
+
+| Criteria | Exemplary | Adequate | Needs Improvement |
+| -------- | --------- | -------- | ----------------- |
+| Train the classifier for multiple fruits | Was able to train the classifier for multiple fruits | Was able to train the classifier for one additional fruit | Was unable to train the classifier for more fruits |
+| Determine how well the classifier works | Was able to comment correctly on how well the classifier worked with different fruits | Was able to observe and offer suggestions as to how well it was working | Was unable to comment on how well the classifier worked |

4-manufacturing/lessons/2-check-fruit-from-device/README.md

@@ -0,0 +1,33 @@
+# Check fruit quality from an IoT device
+
+Add a sketchnote if possible/appropriate
+
+![Embed a video here if available](video-url)
+
+## Pre-lecture quiz
+
+[Pre-lecture quiz](https://brave-island-0b7c7f50f.azurestaticapps.net/quiz/31)
+
+## Introduction
+
+In this lesson you will learn about
+
+In this lesson we'll cover:
+
+* [Thing 1](#thing-1)
+
+## Thing 1
+
+---
+
+## 🚀 Challenge
+
+## Post-lecture quiz
+
+[Post-lecture quiz](https://brave-island-0b7c7f50f.azurestaticapps.net/quiz/32)
+
+## Review & Self Study
+
+## Assignment
+
+[](assignment.md)

4-manufacturing/lessons/2-check-fruit-from-device/assignment.md

@@ -0,0 +1,9 @@
+#
+
+## Instructions
+
+## Rubric
+
+| Criteria | Exemplary | Adequate | Needs Improvement |
+| -------- | --------- | -------- | ----------------- |
+| |  |  |  |

4-manufacturing/lessons/3-run-fruit-detector-edge/README.md

@@ -0,0 +1,33 @@
+# Run your fruit detector on the edge
+
+Add a sketchnote if possible/appropriate
+
+![Embed a video here if available](video-url)
+
+## Pre-lecture quiz
+
+[Pre-lecture quiz](https://brave-island-0b7c7f50f.azurestaticapps.net/quiz/33)
+
+## Introduction
+
+In this lesson you will learn about
+
+In this lesson we'll cover:
+
+* [Thing 1](#thing-1)
+
+## Thing 1
+
+---
+
+## 🚀 Challenge
+
+## Post-lecture quiz
+
+[Post-lecture quiz](https://brave-island-0b7c7f50f.azurestaticapps.net/quiz/34)
+
+## Review & Self Study
+
+## Assignment
+
+[](assignment.md)

4-manufacturing/lessons/3-run-fruit-detector-edge/assignment.md

@@ -0,0 +1,9 @@
+#
+
+## Instructions
+
+## Rubric
+
+| Criteria | Exemplary | Adequate | Needs Improvement |
+| -------- | --------- | -------- | ----------------- |
+| |  |  |  |

4-manufacturing/lessons/4-trigger-fruit-detector/README.md

@@ -0,0 +1,33 @@
+# Trigger fruit quality detection from a sensor
+
+Add a sketchnote if possible/appropriate
+
+![Embed a video here if available](video-url)
+
+## Pre-lecture quiz
+
+[Pre-lecture quiz](https://brave-island-0b7c7f50f.azurestaticapps.net/quiz/35)
+
+## Introduction
+
+In this lesson you will learn about
+
+In this lesson we'll cover:
+
+* [Thing 1](#thing-1)
+
+## Thing 1
+
+---
+
+## 🚀 Challenge
+
+## Post-lecture quiz
+
+[Post-lecture quiz](https://brave-island-0b7c7f50f.azurestaticapps.net/quiz/36)
+
+## Review & Self Study
+
+## Assignment
+
+[](assignment.md)

4-manufacturing/lessons/4-trigger-fruit-detector/assignment.md

@@ -0,0 +1,9 @@
+#
+
+## Instructions
+
+## Rubric
+
+| Criteria | Exemplary | Adequate | Needs Improvement |
+| -------- | --------- | -------- | ----------------- |
+| |  |  |  |

README.md

@@ -79,6 +79,10 @@ We have two choices of IoT hardware to use for the projects depending on persona
 | 12 | [Transport](./3-transport) | Store location data | Learn how to store IoT data to be visualized or analysed later | [Store location data](./3-transport/lessons/2-store-location-data/README.md) |
 | 13 | [Transport](./3-transport) | Visualize location data | Learn about visualizing location data on a map, and how maps represent the real 3d world in 2 dimensions | [Visualize location data](./3-transport/lessons/3-visualize-location-data/README.md) |
 | 14 | [Transport](./3-transport) | Geofences | Learn about geofences, and how they can be used to alert when vehicles in the supply chain are close to their destination | [Geofences](./3-transport/lessons/4-geofences/README.md) |
+| 15 | [Manufacturing](./4-manufacturing) | Train a fruit quality detector | Learn about training an image classifier in the cloud to detect fruit quality | [Train a fruit quality detector](./4-manufacturing/lessons/1-train-fruit-detector/README.md) |
+| 16 | [Manufacturing](./4-manufacturing) | Check fruit quality from an IoT device | Learn about using your fruit quality detector from an IoT device | [Check fruit quality from an IoT device](./4-manufacturing/lessons/2-check-fruit-from-device/README.md) |
+| 17 | [Manufacturing](./4-manufacturing) | Run your fruit detector on the edge | Learn about running your fruit detector on an IoT device on the edge | [Run your fruit detector on the edge](./4-manufacturing/lessons/3-run-fruit-detector-edge/README.md) |
+| 18 | [Manufacturing](./4-manufacturing) | Trigger fruit quality detection from a sensor | Learn about triggering fruit quality detection from a sensor | [Trigger fruit quality detection from a sensor](./4-manufacturing/lessons/4-trigger-fruit-detector/README.md) |
 
 ## Offline access
 

images/icons/noun_tomato_1285672.svg

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