Implementasi Convolutional Neural Network untuk Mengetahui Buah Tomat yang Matang pada Pohon Tomat Menggunakan Perangkat Android

Timothy Christian Yunanto, Kartika Gunadi, Anita Nathania Purbowo


The development of instant era makes people want something that fast and efficient. As we know, picking ripe tomatoes on the tree requires a long time if done by humans. To solve these problems, automatic robots are used that can replace the role of humans. To get a successful automated robot requires the creation of efficient algorithm function (program). The Program can be run on an Android Device. We use Blob Detection method on Computer Vision, and the result will be processed by the Convolutional Neural Network method. CNN method requires to determine whether the object is ripe tomatoes or other objects. Blob Detection is used to detect tomato objects based on previously obtained masks. Before doing the training, it is necessary to make a model that contains convolutional layer, max polling layer, flatten layer, dropout layer, and dense layer. The test is carried out with a scenario study and several cases such as bunched tomatoes, scattered tomatoes, tomatoes whose masks are not oval, and so on. The results show that the results of CNN are very dependent on the results of Blob Detection because the input from CNN is from the result of Blob Detection. If Blob Detection fails to get the tomato object, CNN will not run properly. The results show that Blob Detection will fail to detect the tomato object if the tomato is blocked by another object which causes the mask shape of the object to be chaotic. The test results from CNN also showed an accuracy value of training of 96% and testing accuracy of 93%.


Convolutional Neural Network; Blob Detection; Mature Tomato; Ripe Tomato; Tensorflow; Keras

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