This project was created for my Electrical Engineering Undergraduate Honours Thesis at The University of Queensland which recieved a distinction. The aim of the project was to designed and build a semi-autonomous fertilizer distribution rover as an extension of the farmbot open source project. Feel free to view my Thesis Report.

The following technologies were used:

• Linux shell scripting was used to setup the environment on RasPi to run the python scripts controlling the rover
• Keras and TensorFlow were used to train CNN Deep Learning Networks for Image recognition to identify plants correctly before distributing the fertilizer
• OpenCV was used for preprocessing and transforming image data into Numpy arrays that could be analyzed by the Image recognition Deep Learning Model
• RasPi was interfaced with Arduino microcontroller running C++ code controlling the hardware modules e.g. motors, pumps, wifi.

Please Note more detailed setup is contained in the thesis report. That being said key installations required are:

• Tensorflow
• Keras
• OpenCV
• Numpy
• Raspi Zero W running Debian Linux
• Arduino Uno

This project is licensed under the MIT License

Inspiration and code snippets.

• boppreh for keyboard control python library