Identification Of Tomato Based On Color Using The Backpropagation Artificial Neural Network (Ann) Method

Abstract

The development of industrial agriculture and plantations in Indonesia is growing so rapidly. One of the stages in the processing of plantation products is the selection of products based on their quality (eg fruit ripeness level). The process of selecting agricultural and plantation products is generally very dependent on human perception of the color composition of the image. The manual method is carried out based on direct visual observation of the fruit to be classified. Identification with this method has several weaknesses, including the relatively long time required and the production due to human visual limitations, fatigue levels and differences in perceptions of fruit quality. The development of artificial neural network (ANN) research makes it possible to sort agricultural and plantation products automatically with the help of image processing applications. Identification of tomato fruit maturity applies the backpropagation learning method. Handphone is used for identifying media as taking pictures of tomatoes. The success rate of tomato’s ripeness obtained by using the backpropagation learning method with a success rate of 96%.

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