Komparasi Kinerja DenseNet 121 dan MobileNet untuk Klasifikasi Citra Penyakit Daun Kentang - Umi Khultsum; Ghofar Taufik (JURIKOM (Jurnal Riset Komputer), 2023)
Potato plants are one of the plants that are included in horticultural commodities that are widely cultivated by farmers. Potatoes are the part produced from this plant and are the fourth largest agricultural food crop in the world after corn, wheat and rice. Potato plants are susceptible to attacks by various diseases in the leaf area, resulting in delays in potato production. This disease can be recognized by farmers visually, because the infected leaves have a different color and texture from healthy or fresh leaves. However, it was found that detection using the naked eye by farmers required more processing time and often gave inappropriate results. Methods in the field of image processing can be applied, namely by using pattern recognition or characteristics from the image of diseased potato leaves. Through this technique it is hoped that it can detect diseases on potato leaves correctly and accurately. Based on this description, this study aims to design a Convolution Neural Network (CNN) model and evaluate the performance of two architectures, namely DenseNet 121 and MobileNet. From the results of research conducted by the author on potato leaf disease images, it shows that the MobileNet algorithm is better than the DenseNet 12 algorithm. The MobileNet algorithm produces an accuracy of 98.00%, therefore the MobileNet algorithm has better performance for image classification of potato leaf disease