Regresar

Open Source System for Identification of Maize Leaf Chlorophyll Contents Based on Multispectral Images

Abstract:

It is important for farmers to know the level of chlorophyll in plants since this depends on the treatment they should give to their crops. There are two common classic methods to get chlorophyll values: from laboratory analysis and electronic devices. Both methods obtain the chlorophyll level of one sample at a time, although they can be destructive. The objective of this research is to develop a system that allows for obtaining the chlorophyll level of plants using multispectral images. Python programming language and different libraries of that language were used to develop the solution. It was implemented as an image labeling module, a simple linear regression, and a pbkp_rediction module. The first module was used to create a database that relates the values of the NDVI image with those of chlorophyll, which was then used to obtain a linear regression model for the pbkp_rediction system to obtain chlorophyll values from the images. The model was trained with 92 images and was obtained a root-mean-square error (RMSE) of 7.27 units CCM (Chlorophyll Content Meter). While the testing was performed using 10 values obtaining a maximum error of 15.5%. It is concluded that the system is appropriate for chlorophyll contents identification on maize leaves in field tests. However, it can also be adapted for other measurements and crops. The system can be downloaded at [1].