Leveraging SVM for Precision Agriculture: A Case Study in Orange Ripeness Classification,

Access the full open-source code on GitHub:
https://github.com/neonicX-Tech/Class...

Learn Orange Ripeness Classification with SVM: A Deep Dive

Discover how to leverage Support Vector Machines (SVM) for accurate orange ripeness classification using this in-depth tutorial. Join Vahid from neonicX as he guides you through the process, from dataset collection to model training and evaluation.

Key Highlights:

Code Repository: Access the complete code on our GitHub repository.

Two SVM Implementations: Choose between scikit-learn and OpenCV.

Customizable Dataset: Use our provided dataset or train your own.

Step-by-Step Training: Follow detailed instructions for cloning, creating a virtual environment, and installing requirements.

SVM Model Exploration: Understand the pre-trained SVM model with a poly kernel.

Parameter Tuning: Customize kernel, gamma, and C flags to tailor the model to your specific needs.

Efficient Classification: Use the trained model to classify oranges in images with high accuracy.

Visual Output: View classified images with the --view flag for a clear understanding of results.