PENGARUH ARSITEKTUR CONVOLUTIONAL NEURAL NETWORK UNTUK KLASIFIKASI PENYAKIT DAUN TOMAT - Rizky Arya Kurniawan; Sunyoto, Andi; Nasiri, Asro (TEKNIMEDIA: Teknologi Informasi dan Multimedia, 2023)
Plant disease is one of the crucial factors in plant survival. Tomato plants also need early help to be able to deal with disease problems. One of the organs of the tomato plant that is commonly attacked by diseases is the leaves. By providing assistance early on, it can prevent crop failure. Of course, having a trained system can reduce the cost of a farmer in dealing with diseases without expert assistance. In this research, we will test the ability of the CNN architecture to classify tomato leaf disease images. The dataset used is 4079 image data which are divided into 3 disease classes. From the results of experiments that have been carried out the InceptionV3 architecture gets the best results with an accuracy rate of 100%, ResNet50 has 97,36% accuracy and MobileNet 85,81%.