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

Author: Jim Bennett

.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)