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...
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...
Recent developments in computer vision and artificial intelligence have made it possible to achieve classifications of complex patterns in images. These developments have made the successful implementation of this solution architecture achievable. My implementation does not represent a production-ready product as it is a PoC. However, it does prove that a product resulting from this solution is achievable. With this implementation it is safe to say that all technical requirements have been satisfied to an acceptable degree for this thesis. Future To achieve a production ready product a few improvements must be implemented. Legal Compliance in general and specifically with regards to privacy/integrity (GDPR) and Data Storage laws must be researched and implemented. 2. A feature must be implemented in the AI implementation that allows for live training to support continuous improvement of the AI. This live training will lead to e...
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