Prediction of the chlorophyll content in pomegranate leaves based on digital image processing technology and stacked sparse autoencoder
Peng, Yingshu | Wang, Yi
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.
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