PDF(Pubmed)
Abstract：
BACKGROUND: The phenotypic traits of leaves are the direct reflection of the agronomic traits in the growth process of leafy vegetables, which plays a vital role in the selection of high-quality leafy vegetable varieties. The current image-based phenotypic traits extraction research mainly focuses on the morphological and structural traits of plants or leaves, and there are few studies on the phenotypes of physiological traits of leaves. The current research has developed a deep learning model aimed at predicting the total chlorophyll of greenhouse lettuce directly from the full spectrum of hyperspectral images.
RESULTS: A CNN-based one-dimensional deep learning model with spectral attention module was utilized for the estimate of the total chlorophyll of greenhouse lettuce from the full spectrum of hyperspectral images. Experimental results demonstrate that the deep neural network with spectral attention module outperformed the existing standard approaches, including partial least squares regression (PLSR) and random forest (RF), with an average R2 of 0.746 and an average RMSE of 2.018.
CONCLUSIONS: 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.
RESULTS: A CNN-based one-dimensional deep learning model with spectral attention module was utilized for the estimate of the total chlorophyll of greenhouse lettuce from the full spectrum of hyperspectral images. Experimental results demonstrate that the deep neural network with spectral attention module outperformed the existing standard approaches, including partial least squares regression (PLSR) and random forest (RF), with an average R2 of 0.746 and an average RMSE of 2.018.
CONCLUSIONS: 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.
摘要：
背景：叶片的表型性状是叶类蔬菜生长过程中农艺性状的直接反映，对优质叶类蔬菜品种的选择起着至关重要的作用。目前基于图像的表型性状提取研究主要集中在植物或叶片的形态和结构性状,关于叶片生理性状表型的研究较少。目前的研究已经开发了一种深度学习模型，旨在直接从高光谱图像的全光谱中预测温室莴苣的总叶绿素。
结果：基于CNN的一维深度学习模型与光谱注意模块用于从高光谱图像的全光谱估计温室莴苣的总叶绿素。实验结果表明,带光谱注意模块的深度神经网络优于现有的标准方法,包括偏最小二乘回归(PLSR)和随机森林(RF)，平均R2为0.746，平均RMSE为2.018。
结论：这项研究揭示了利用深度关注网络和高光谱成像来估计莴苣叶绿素水平的能力。这种方法提供了一种方便的,非破坏性的，叶类蔬菜的自动监测和生产管理的有效估算方法。
结果：基于CNN的一维深度学习模型与光谱注意模块用于从高光谱图像的全光谱估计温室莴苣的总叶绿素。实验结果表明,带光谱注意模块的深度神经网络优于现有的标准方法,包括偏最小二乘回归(PLSR)和随机森林(RF)，平均R2为0.746，平均RMSE为2.018。
结论：这项研究揭示了利用深度关注网络和高光谱成像来估计莴苣叶绿素水平的能力。这种方法提供了一种方便的,非破坏性的，叶类蔬菜的自动监测和生产管理的有效估算方法。
