Data-Driven Prediction of Grape Leaf Chlorophyll Content Using Hyperspectral Imaging and Convolutional Neural Networks

Data-Driven Prediction of Grape Leaf Chlorophyll Content Using Hyperspectral Imaging and Convolutional Neural Networks

Grapes, highly nutritious and flavorful fruits, require adequate chlorophyll to ensure normal growth and development. Consequently, the rapid, accurate, and efficient detection of chlorophyll content is essential. This study develops a data-driven integrated framework that combines hyperspectral ima...

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Bibliographic Details
Main Authors: Minglu Zeng, Xinghui Zhu, Ling Wan, Jian Xu, Luming Shen
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Applied Sciences
Subjects:
hyperspectral imaging
deep learning
convolutional neural network
machine learning
grape leaves
chlorophyll content
Online Access:https://www.mdpi.com/2076-3417/15/10/5696
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