The recognition of mango varieties based on the leaves shape and texture using back propagation neural network method
At this time, the demand of Indonesian mango is in great demand by the society especially for the superior quality mango like Manalagi and Gadung. However, many people are still wrong in distinguishing mango varieties. At this moment, the identification or introduction of mango varieties is done by eye. Some people may be expert in identifying mango varieties based on leaves by eye, but not all mango varieties they can identify. Until now, there are several methods to identify mango varieties, but the accuracy got is less than 80% In research before, the extraction feature used is either shape or texture feature of the leaf images. In this research, we use Backpropagation Neural Network (BPNN) by using mango leaf shape and texture feature. A Dataset used are 300 images of mango leaves consisting of 150 images of mango leaves of Manalagi varieties and 150 images of leaves Gadung. By using this method, we obtain that the most optimal BPNN model got by using hidden layer 19, learning rate 0.9, momentum 0.9, and epoch 100 with root mean square error (RMSE) 0.0018. The accuracy rate that we obtain is 96%
2017 International Conference on Sustainable Information Engineering and Technology (SIET)