Adding structure for project 4

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Jim Bennett 4 years ago
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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

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# 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 conver belt and staff 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 LEDs and 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. 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)
As advances happen in AI, these machines can become more advanced, using 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.
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)

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# Train a fruit quality detector
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/29)
## 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/30)
## Review & Self Study
## Assignment
[](assignment.md)

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#
## Instructions
## Rubric
| Criteria | Exemplary | Adequate | Needs Improvement |
| -------- | --------- | -------- | ----------------- |
| | | | |

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# 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)

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

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# 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)

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

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# 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)

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

@ -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) |
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