A hyperspectral deep learning attention model for predicting lettuce chlorophyll content.

Ziran YeXiangfeng TanMengdi DaiXuting ChenYuanxiang ZhongYi ZhangYunjie RuanDedong Kong

Published in: Plant methods (2024)

This study unveils the capability of leveraging deep attention networks and hyperspectral imaging for estimating lettuce chlorophyll levels. This approach offers a convenient, non-destructive, and effective estimation method for the automatic monitoring and production management of leafy vegetables.

Keyphrases

deep learning
working memory
machine learning
high resolution
artificial intelligence
convolutional neural network
energy transfer
risk assessment
mass spectrometry
human health
photodynamic therapy