Detecting Weeds in Crops using PyTorch*: Developer Spotlight

Weeds are the harmful agriculture pests that seriously affect the crops. Weeds are responsible for hindering the growth of crops by stealing nutrients, sunlight, and water from the plants. Early detection of weeds using computer vision helps farmers take targeted actions to control and remove them, resulting in higher crop yields. Also, computer vision helps in identifying and mapping weed distribution. This allows mechanical weeding (weed control method) by reducing herbicide use, saving time, and reducing labor costs.

Ashhadul Islam, in his blog proposed a solution for detecting weeds in crops using Intel® Extension for PyTorch* and Intel® Developer Cloud.

The blog explains the various steps involved in the project:

1. Setup the environment for the project
2. Data Preprocessing
3. Model Training
4. Model Testing

Read more about the project on Medium and GitHub.

Learn more about Intel Extension for PyTorch!!!

Intel Extension for PyTorch: The Intel extension expands PyTorch with up-to-date features and optimizations for an extra performance boost on Intel hardware. Check out how to install Intel Extension for PyTorch. The extension can be loaded as a Python module or linked as a C++ library. Python users can enable it dynamically by importing intel_extension_for_pytorch.

• The CPU tutorial gives detailed information about Intel Extension for PyTorch for Intel CPUs. Source code is available at the main branch.
• The GPU tutorial gives detailed information about Intel Extension for PyTorch for Intel GPUs. The source code is available at the xpu-main branch.

What’s Next?

We encourage you to check out and incorporate Intel’s other AI/ML Framework optimizations and end-to-end portfolio of tools into your AI workflow and learn about the unified, open, standards-based oneAPI programming model that forms the foundation of Intel’s AI Software Portfolio to help you prepare, build, deploy, and scale your AI solutions.

About the Author:

Ashhadul Islam is a PhD graduate from Hamad Bin Khalifa University. His research includes optimizing deep neural networks, feature extraction, and data augmentation. He has played different roles in the data science pipeline in companies like Redhat, the Development Bank of Singapore, and National Instruments.