Real-Time Palm Oil Fresh Fruit Bunches (FFB) Grading with Computer vision
Evolving from Manual to AI-Driven FFB Grading with Computer Vision
In traditional palm oil production, workers grade fruit by eye, judging ripeness and quality manually. This method is slow, sometime inaccurate, and results can vary from person to person.
Now, we offer a smarter solution: automated AI grading powered by computer vision. This shift means grading is not only faster but also far more precise and reliable. AI grading evaluates every fruit real-time with unmatched accuracy, ensuring top-notch quality control in oil production.
Ready to see the difference? Send us images/videos of your palm oil fresh fruit bunches (FFB) and let our AI tool show you what it can do.
How FFB Grading with Computer Vision works
System Concept
The system works by capturing image or video using camera then processed by the AI application to determine the fruit bunch and it’s ripenesses in form of bounding box as next. It’s support multiple object detection at the same time.
+ AI Model
Bunch Detection
This process involves using AI to detect palm fruit bunches within images or video streams. It leverages computer vision techniques to identify and segment fruit bunches as distinct objects from the background, allowing for precise identification in various conditions.
Classification & AI Model Training
After object recognition, the solution classifies the ripeness of each fruit bunch. This involves training an AI model on a labeled dataset where images are tagged according to ripeness levels. Machine learning algorithms analyze the dataset to learn patterns and features that correspond to different ripeness stages, enabling the model to accurately classify new images.
Our current model has been trained using 1000+ images and videos.
Successful Results
Look at these videos showling that our computer vision program is capable of correctly determining the fruit's Ripeness.
Unripe
Ripe
Overripe
