Factor to consider when building machine learning(AI) apps for Africa

So l have been working on a machine learning app to help African farmers detect plant diseases using machine learning and their smartphone camera and unlike developing machine learning systems for developed countries in Africa we have a wide range of problems such as high cost of broadband , limited internet coverage, lowend smart phones which translates to limited storage on user’s devices.

1. Africa is mobile first economy

   Africa is mobile first economy , that means a lot of people turn to their smartphones for business learning …etc .This means that if you are developing machine learning solutions that are going to serve a lot of people these solutions must be fully optimized for mobile devices .When l developed Dr Plant X ( an app that used machine learning to help farmers detect plant diseases) l emphasized on a mobile first approach because that many farmers have a smartphone and serving ML via mobile devices would help a lot of farmers rather than using the web.

2. High cost of internet and limited internet coverage

   It’s not a secret that data is very expensive in Africa as compared to other continents and also broadband coverage is poor in some areas . Thus if one is to develop machine learning solutions in such an environment one must consider a on device approach (inference takes place on the device ),which eliminates the need to communicate with a server which saves user’s data and allows the app to be used in remote areas .For example with Dr Plant X some farmers are in remote areas which would be difficult for them to use the app if communicated with an external server , but with on device ML powered with Tensorflow lite ,they can use the application even in remote areas once the app is downloaded .

3. Application Size

   As l have highlighted before people in Africa don’t have the luxury of installing large apps on their devices , thus if you are a machine learning developer developing machine learning solutions for Africa you must optimize your ML model size so that it will not result in a huge app size.When we developed Dr Plant X , we had to use the mobile net architecture , which is an architecture to build light weight deep neural networks that do not have a large size . So if you are going to develop ML solutions for Africa choose architectures with few parameters that do not result in large models

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