Using Data-Driven Computer Vision Techniques to Improve Wheat Yield Prediction

Using Data-Driven Computer Vision Techniques to Improve Wheat Yield Prediction

Accurate ear counting is essential for determining wheat yield, but traditional manual methods are labour-intensive and time-consuming. This study introduces an innovative approach by developing an automatic ear-counting system that leverages machine learning techniques applied to high-resolution im...

Full description

Saved in:
Bibliographic Details
Main Authors: Merima Smajlhodžić-Deljo, Madžida Hundur Hiyari, Lejla Gurbeta Pokvić, Nejra Merdović, Faruk Bećirović, Lemana Spahić, Željana Grbović, Dimitrije Stefanović, Ivana Miličić, Oskar Marko
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:AgriEngineering
Subjects:
precision agriculture
wheat ear counting
artificial intelligence
computer vision
real-time processing
detection
Online Access:https://www.mdpi.com/2624-7402/6/4/269
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2624-7402/6/4/269

Similar Items