Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder
Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder
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DOI：
10.1080/10942912.2019.1675692
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发表时间：
2019-01
影响因子：
2.9
通讯作者：
Yingshu Peng;Yi Wang
中科院分区：
文献类型：
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作者：
Yingshu Peng;Yi Wang
基于数字图像分析的叶片叶绿素预测大多采用人工提取特征和传统的机器学习方法。本研究基于数字图像处理技术，对图像进行阈值分割、噪声处理、背景分离等一系列预处理操作，以去除背景和噪声干扰。通过堆叠稀疏自编码器（SSAE）网络自动学习叶片RGB图像的内在特征，以获得简洁的数据特征。最后，建立了叶片RGB图像特征与其SPAD值（任意单位）之间的预测模型，用于预测植物叶片叶绿素含量。结果表明，本研究中深度神经网络检测叶绿素含量的准确性和自动化程度均高于传统的机器学习方法。
ABSTRACT Most leaf chlorophyll predictions based on digital image analyzes are modeled by manual extraction features and traditional machine learning methods. In this study, a series of image preprocessing operations, such as image threshold segmentation, noise processing, and background separation, were performed based on digital image processing technology to remove the background and noise interference. The intrinsic features of the leaf RGB image were automatically learned through a stacked sparse autoencoder (SSAE) network to obtain concise data features. Finally, a prediction model between the RGB image features of a leaf and its SPAD value (arbitrary units) was established to predict the chlorophyll content in the plant leaf. The results show that the accuracy and automation of the detection of chlorophyll content of the deep neural network in this study are higher than those of traditional machine learning methods.