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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 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 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 through artificial neural network 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 0.0921 with regression testing of 0.8468. Network model is able to read the highest source of chlorophyll on cassava leaf with 84.68%.