Grape Vision: A CNN-Based System for Yield Component Analysis of Grape Clusters
Keywords:
Grape prediction, CNN, machine learning, image processingAbstract
The agricultural industry is adopting advanced technologies and applications like yield prediction, precision agriculture, and automated harvesting to enhance production and quality. Machine learning (ML) and computer vision are increasingly used for fruit detection, segmentation, and counting. Specifically, the use of Convolutional Neural Networks (CNN) in grape yield prediction and quality assessment is gaining popularity due to its high accuracy and cost efficiency. Additionally, a new methodology based on image analysis has been developed for fast and inexpensive cluster yield component determination in the wine and table grape industry.
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