Final Work: PlantCare

In this blogpost I will cover the different tools I have used during the development process of this project. General The tool below is used in all implementations. Github This free hosting website was used for version control. It uses the git open-source VCS (Version Controll System) enabling a combination of both local and centralized version control. This allows for easy and efficient management of progress in development. AIDE Implementation AIDE or the AI Diagnosis Engine includes the pre-trained AI model. This model allows the diagnosis engine from the previous section to calculate the disease prediction rankings. It also handles the image pre-processing of incoming requests. The following components are involved in this process. Anaconda Distribution This tool is a python package manager and provides the ability to easily manage, test and deploy development environments. Pycharm This IDE made by the software company JetBrains, is used for Python...

To start with the AI implementation of this project, I researched some similar exisiting projects until I found an open-source project on GitHub  from Marko Arsenovic, that was comparing different results achieved by AI models using a plant disease dataset. This was an ideal starting point for my own project. A chart was also provided to clarify the results achieved by his project. I noticied that AlexNet achieved good results while not taking a lot of time to train as you can see in the chart below provided by Marko Arsenovic. This project used an open-source dataset provided by the R&D unit of Penn State University called PlantVillage, to train the neural networks in plant disease identification. This dataset includes 38 classifications each represented by more than 1000 pictures and spread over 14 plant types. In order to verify that our AI model is performing correctly it is important to keep some data for validation. Thus, 80% of plant disease images was used fo...

Starting from this blog on I will switch to English as this makes my writing process much more efficiënt. In this blogpost I will describe one of the most import components of my project, the AI Diagnosis Engine. In the previous post, I wrote a short paragraph about the high level functionality this major component offers and what factor it depends on. Today I will talk about all the sub-components and relations/interactions between them.  Plant Deficiëntie Dataset The first sub-component of AIDE that I will cover is also the most critical part. The PDD (Plant deficiency dataset) is a dataset with labelled images representing different disease-plant combinations. The quality of the PDD is heavily reliant on the quality and quantity of labelled pictures it contains. This dataset is eventually used to train the AI model that will be used in the following sub-component to process analysis requests from the end-user. Actor Model This sub-component represents the e...