The artificial neural network to predict chlorophyll content of cassava (Manihot esculenta) leaf

Abstract

Artificial neural network (ANN) based prediction system was presented for predicting the leaf population chlorophyll content from cassava leaf images. As the training of this prediction system relied heavily on how well those leaf green pixels were separated from background noises in cassava leaf images, a global thresholding algorithm and an omnidirectional scan noise filtering coupled with the hue histogram statistic method were designed for leaf green pixel extraction. With the obtained of leaf green pixels, the system training was carried out by applying a back-propagation algorithm. The system was tested to predict the chlorophyll content from the cassava leaf images. The purpose of this research was to find the relationship between the color index Red, Green, Blue (RGB); Hue; Saturation HSV; Value; Saturation HSL and Lightness to chlorophyll, and to find the appropriate form of ANN to predict the highest chlorophyll source in cassava leaf based on digital images. The results showed highest positive regression occurred in the saturation HSL index against the total chlorophyll of cassava leaf as much as 78.6%. The best model produced using ANN methods in predicting total chlorophyll is a network model with 8 inputs, 9 hidden layers and one output layer, in the proportion of training data 75% testing data 25% have value result the smallest MSE testing is 0.092 with regression testing of 0.847. Network model can read the highest source of chlorophyll on cassava leaf with the value of 84.68%.