Author(s): Souradip Pal

Originally published on Towards AI.

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In the fast-paced world of agriculture, being able to classify different rice varieties quickly and accurately can be a game-changer. But how can we harness machine learning for something as niche as rice classification? Well, this is where PyTorch, a powerful deep learning library, steps in. Today, I’ll guide you through creating a Convolutional Neural Network (CNN) using PyTorch to classify rice varieties based on images. This hands-on tutorial is designed for anyone with a basic understanding of Python, and I’ll walk you through each step of the code so you can follow along effortlessly.

Before diving in, make sure you have the necessary libraries installed. Run the following command to install any missing dependencies:

pip install torch torchvision pandas numpy seaborn matplotlib splitfolders tabulate termcolor scikit-learn

With these installed, you’re all set to start coding!

We’ll use a rice image dataset to train our model. You can find various datasets online, but a great choice for simplicity and consistency is to use Kaggle. If you have a Kaggle account, you can directly import datasets into your notebook without downloading them locally.

If you’re working in a Kaggle notebook, just make sure to upload… Read the full blog for free on Medium.

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Published via Towards AI