Towards a Crowdsourced Digital Coffee Atlas for Sustainable Coffee Farming
The present work summarizes the results of a 15-week student project addressing the field of sustainable coffee farming. Coffee farmers often lack scientific knowledge concerning the coffee varieties they cultivate, and having grown coffee for generations, they often have limited knowledge concernin...
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MDPI AG
2024-09-01
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| author | Emma Krischkowsky Onur Bal Colin Beyer David Miller Manuel Walter Kirstin Kohler |
| author_facet | Emma Krischkowsky Onur Bal Colin Beyer David Miller Manuel Walter Kirstin Kohler |
| author_sort | Emma Krischkowsky |
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| description | The present work summarizes the results of a 15-week student project addressing the field of sustainable coffee farming. Coffee farmers often lack scientific knowledge concerning the coffee varieties they cultivate, and having grown coffee for generations, they often have limited knowledge concerning the names of their coffee varieties used on the global market. This leads to significant disadvantages in market positioning. Consequently, farmers often receive lower prices for their coffee as they cannot accurately determine its true market value. In addition, the effects of climate change force farmers to reconsider the varieties they cultivate, as they cannot exhibit stable yield performance due to the changed climate. If farmers are unaware of the potential quality advantages of different coffee types, this prevents them from optimizing growing conditions specific to their climate. As part of a design thinking-based project course, a team of four design and computer science students at Hochschule Mannheim searched for a solution on how to overcome the aforementioned disadvantages for local coffee farmers with the support of digital technology. Coffee Consulate helped the team by connecting them to farmers around the world and sharing their domain knowledge. The student team’s main idea is to bridge the aforementioned knowledge gap by collecting globally distributed data about coffee species in one worldwide accessible, digital system, allowing farmers to be globally connected. Their concept proposes a digital Coffee Atlas for mobile phones, showing where on the planet and under which climate conditions coffee varieties are grown and how these species are named on the global market. The app allows one to identify coffee plants based on pictures uploaded from farmers’ phones. The team developed an implementation roadmap that considered how to subsequently extend the database behind the Coffee Atlas and how to accelerate the crowdsourcing process. AI-based image recognition trained with pictures taken from a living collection of coffee cultivars, like in the botanical garden of Wilhelma (Stuttgart, Germany), and DNA sequences could serve as an initial step for creating the database. Farmers should be motivated to upload pictures of their plants by additional services provided by the app. Therefore, information about coffee species can be crowdsourced with the help of farmers around the world. Such services could include the recognition of plant health conditions, as well as the estimation of the actual market price of a species based on the identification of coffee varieties or the recommendation of species that are better adapted to the actual or expected climate. In its final implementation, the Coffee Atlas will enhance agricultural practices and economic outcomes for farmers and provide a valuable source of data for researchers around the world. |
| format | Article |
| id | doaj-art-9ecd7d7d7e1944b7b385a9cee0132551 |
| institution | Kabale University |
| issn | 2504-3900 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Proceedings |
| spelling | doaj-art-9ecd7d7d7e1944b7b385a9cee01325512024-12-27T14:48:24ZengMDPI AGProceedings2504-39002024-09-011091510.3390/ICC2024-18176Towards a Crowdsourced Digital Coffee Atlas for Sustainable Coffee FarmingEmma Krischkowsky0Onur Bal1Colin Beyer2David Miller3Manuel Walter4Kirstin Kohler5Inno.space—Design Factory Mannheim, University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, 68163 Mannheim, GermanyInno.space—Design Factory Mannheim, University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, 68163 Mannheim, GermanyInno.space—Design Factory Mannheim, University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, 68163 Mannheim, GermanyInno.space—Design Factory Mannheim, University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, 68163 Mannheim, GermanyInno.space—Design Factory Mannheim, University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, 68163 Mannheim, GermanyInno.space—Design Factory Mannheim, University of Applied Sciences Mannheim, Paul-Wittsack-Straße 10, 68163 Mannheim, GermanyThe present work summarizes the results of a 15-week student project addressing the field of sustainable coffee farming. Coffee farmers often lack scientific knowledge concerning the coffee varieties they cultivate, and having grown coffee for generations, they often have limited knowledge concerning the names of their coffee varieties used on the global market. This leads to significant disadvantages in market positioning. Consequently, farmers often receive lower prices for their coffee as they cannot accurately determine its true market value. In addition, the effects of climate change force farmers to reconsider the varieties they cultivate, as they cannot exhibit stable yield performance due to the changed climate. If farmers are unaware of the potential quality advantages of different coffee types, this prevents them from optimizing growing conditions specific to their climate. As part of a design thinking-based project course, a team of four design and computer science students at Hochschule Mannheim searched for a solution on how to overcome the aforementioned disadvantages for local coffee farmers with the support of digital technology. Coffee Consulate helped the team by connecting them to farmers around the world and sharing their domain knowledge. The student team’s main idea is to bridge the aforementioned knowledge gap by collecting globally distributed data about coffee species in one worldwide accessible, digital system, allowing farmers to be globally connected. Their concept proposes a digital Coffee Atlas for mobile phones, showing where on the planet and under which climate conditions coffee varieties are grown and how these species are named on the global market. The app allows one to identify coffee plants based on pictures uploaded from farmers’ phones. The team developed an implementation roadmap that considered how to subsequently extend the database behind the Coffee Atlas and how to accelerate the crowdsourcing process. AI-based image recognition trained with pictures taken from a living collection of coffee cultivars, like in the botanical garden of Wilhelma (Stuttgart, Germany), and DNA sequences could serve as an initial step for creating the database. Farmers should be motivated to upload pictures of their plants by additional services provided by the app. Therefore, information about coffee species can be crowdsourced with the help of farmers around the world. Such services could include the recognition of plant health conditions, as well as the estimation of the actual market price of a species based on the identification of coffee varieties or the recommendation of species that are better adapted to the actual or expected climate. In its final implementation, the Coffee Atlas will enhance agricultural practices and economic outcomes for farmers and provide a valuable source of data for researchers around the world.https://www.mdpi.com/2504-3900/109/1/5artificial intelligencecoffee varietiessustainable farmingdigital solutions |
| spellingShingle | Emma Krischkowsky Onur Bal Colin Beyer David Miller Manuel Walter Kirstin Kohler Towards a Crowdsourced Digital Coffee Atlas for Sustainable Coffee Farming Proceedings artificial intelligence coffee varieties sustainable farming digital solutions |
| title | Towards a Crowdsourced Digital Coffee Atlas for Sustainable Coffee Farming |
| title_full | Towards a Crowdsourced Digital Coffee Atlas for Sustainable Coffee Farming |
| title_fullStr | Towards a Crowdsourced Digital Coffee Atlas for Sustainable Coffee Farming |
| title_full_unstemmed | Towards a Crowdsourced Digital Coffee Atlas for Sustainable Coffee Farming |
| title_short | Towards a Crowdsourced Digital Coffee Atlas for Sustainable Coffee Farming |
| title_sort | towards a crowdsourced digital coffee atlas for sustainable coffee farming |
| topic | artificial intelligence coffee varieties sustainable farming digital solutions |
| url | https://www.mdpi.com/2504-3900/109/1/5 |
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