Project Details

• Category: Machine Learning | Data Cleaning | TensorFlow
• Client: Research Work
• Project date: August, 2022
• Project URL: GitHub

This project aims to develop a model for weed detection in agricultural fields using satellite imagery and the latest TensorFlow and Keras technologies. By utilizing convolutional neural networks, the model will be able to identify and classify weeds with high accuracy. The model will be trained using a large dataset of satellite images, with each image being labeled with the location and type of weeds present. After the model is trained, it will be tested on a separate dataset to measure its accuracy. The results of this project will provide a valuable tool for farmers, allowing them to quickly and accurately identify and remove weeds from their fields.