Research on Predicting Modeling for Chlorophyll Contents of Greenhouse Tomato Leaves Based on Multi-Spectral Imaging

Traditional spectrum analysis technology has low accuracy for forecasting chlorophyll content of plants. Research based on 3CCD camera has the limitations of high cost and the number of sensitive wavelengths not adjustable. The present paper develops a new approach to forecasting the chlorophyll content of tomato leaves by the image gray value of the selected sensitive wavelengths (532, 610 and 700 nm). Three common methods such as multi-linear regression, principal component analysis and partial least square regression were employed in forecast modeling, the good results were obtained, and both R-c(2) and R-v(2), reached about 0.9. The method has proven effective and feasible for prediction of chlorophyll contents of tomato leaves, which also lays the foundation for the development of testing instruments for the growing of crops.