IDENTIFIKASI JENIS TANAH MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) DENGAN ARSITEKTUR RESIDUAL NETWORK (RESNET-50) DAN MOBILE NETWORK (MOBILENETV2) - Syarifah, Naily; Mudjirahardjo, Panca; Razak, Angger Abdul (Jurnal Mahasiswa TEUB, 2024)
The research was conducted to identify soil types using artificial intelligence using the Convolutional Neural Network method.(CNN). This is done to help young farm activists stay up-to-date to get land use information, i.e. by helping in optimizing theidentification of land or land as a growing medium. The study uses the MobileNetV2 and ResNet-50 architectures to identify different soil types. Both architectures compared their performance in identifying soil types through the texture and colour taken on the test image set data. Before doing the training of course the data will be used through pre-processing for the consistency of input and maximize the modeling process. Both were tested by performing several scenarios to obtain each the best performance results of the optimizer, the number of epochs and the learning rate values. Models of both architectures have a high degree of accuracy and precision. 3. For the MobileNet architecture, the V2 produced models with accuracy values of 91.91%, loss of 90.87%, and a prediction time of 0.108 seconds. And for the ResNet-50 architecture the model produced a precision value of 99.08%, precision 99.11%, recall 99.12%, F1-score 99.10%, specification 99.85%, loss 5.02% and forecast time of 0.037 seconds. Keywords: CNN, Soil Classification, MobileNetV2, ResNet-50