3D Rover Mapping for Precision Farming

Agriculture plays a key role in producing food globally, but plant diseases can seriously affect how much we can grow. This leads to losing money and puts food supplies at risk. Normally, identifying plant diseases requires someone to inspect the plants manually, which is hard work, takes a lot of time, and can lead to errors. This project proposes using machine learning to solve this issue. It focuses on Convolutional Neural Networks (CNN) to automatically detect plant diseases by analyzing images. We gather a large set of images showing diseased plant leaves to train and test the model, making the process faster and more accurate.