Representation of Elaeis Guineensis nutrition deficiency based on image processing approach

Nutrient deficiencies are one of the common issues faced by Elaeis Guineensis or widely known as oil palm. In this paper, image processing technique is utilized to develop method that is able to represent symptoms of nutrient disease such as nitrogen, potassium and magnesium. Hence, algorithm is developed to process the captured images of the diseased leaves through image segmentation and feature extraction based on nonlinear spatial filtering, YCbCr colour and gray scale morphology method. Experimental results demonstrated that the developed algorithm is capable to represent nutrient deficiencies as visualized by expert vision.