RANCANG BANGUN APLIKASI IDENTIFIKASI PENYAKIT TANAMAN PEPAYA CALIFORNIA BERBASIS ANDROID MENGGUNAKAN METODE CNN MODEL ARSITEKTUR SQUEEZENET - Angga Irawan, Ferry; Sudarma, Made; Care Khrisne, Duman (Jurnal SPEKTRUM; Vol 8 No 2 (2021): Jurnal SPEKTRUM, 2021)
The problem that often occurs in the Agricultural Experimental Garden of Udayana University, especially in the field of agricultural crops in holticulture is about plant diseases, this causes a decrease in production results, so the need for early diagnosis of diseases of plants. The study focused on California papaya plants. Diseases of this plant often appear on the leaves and fruits. With advances in technology in the field of image processing can help problems that occur in the field of agriculture. In this study build an Android application with CNN (Convolutional Neural Network) method using SqueezeNet architecture. Classifying diseases in this plant are Anthracnose, and Ringspot Viruse, as well as classifying healthy papaya. Based on the validation results, the application built using CNN method and SqueezeNet Architecture, can recognize Anthracnose disease, Ringspot Viruse and Papaya healthy through leaves with accuracy of 97% while through fruit accuracy reaches 70%.