Intelligent image analysis for retrieval of leaf chlorophyll content of rice from digital images of smartphone under natural light

Abstract

The present study describes a new imaging method to acquire rice leaf images under field conditions using a smartphone and modeling approaches to retrieve the leaf chlorophyll (Chl) content from digitized images. Pearson's correlation of image-based color indices of the relative Chl content measured with Soil Plant Analysis Development (SPAD) indicated the suitability of the color models RGB, rgb, and DGCI-rgb. Among the linear regression models, the models based on mean brightness ratio (rgb) alone or in combination with a dark green color index (DGCI-rgb) show a good correlation between the predicted Chl content and relative Chl content. A feed-forward backpropagation-type network was also developed following the optimization of hidden neurons, training, and transfer functions. The predicted Chl contents showed a good correlation with SPAD values. Compared to the linear regression model, the developed artificial neural network model was found to be more efficient in predicting the Chl content, particularly with RGB index.