Linear regression and machine learning modelling for chlorophyll content estimation using leaf red, green and blue images

Plant chlorophyll content is a key indicator of crop quality status. The conventional methods and procedures for determining plant chlorophyll content are laborious, time-consuming and costly. However, with the adaptation to machine learning, plant data can be analysed more proficiently using red, green and blue (RGB) images obtained from a smartphone camera. Therefore, this study aimed to utilise machine learning algorithms to predict the chlorophyll content of lettuce based on RGB leaf images. Machine learning algorithms were run using RapidMiner software on indices of 60 images. The actual leaf chlorophyll content was measured using a SPAD chlorophyll meter. The correlation of the ratio between the green channel and red channel (GDR) indices with the leaf chlorophyll content, obtained using linear regression, was around 79.91%, with the lowest Root Mean Square Error (RMSE) of 6.62 g of chlorophyll/100 g fresh tissue. The use of machine learning algorithms with principal component analysis (PCA) increased the estimation accuracy by as much as 24%. The greatest accuracy was achieved using the Support Vector Machine (SVM) algorithm with selected highly correlated image indices, resulting in the lowest RMSE of 5.07 g of chlorophyll/100 g of fresh tissue.

Text 125749.pdf - Published Version Restricted to Repository staff only Download (466kB) Official URL or Download Paper: https://www.myfoodresearch.com/uploads/8/4/8/5/848...

Actions (login required)

View Item