Model Identifikasi Penyakit Pada Tumbuhan Padi Berbasiskan DenseNet - Pailus, Muhammad; Fudholi, Dhomas Hatta; Hidayat, Syarif (J-SAKTI (Jurnal Sains Komputer dan Informatika), 2022)
Errors in identifying diseases in rice plants can cause the potential for crop failure to increase by 18-80%, according to data from the Indonesian Ministry of Agriculture. This could be due to the lack of expertise in agriculture when compared to the amount of land in Indonesia. Recent research in the field of deep learning using neural networks has achieved remarkable improvements. Research on the identification of plant diseases in rice plants, using the MobileNet, NasNet and SqueezeNet architecture that supports mobile devices has been carried out. The experimental results show that the proposed architecture can achieve an accuracy of 93.3%. Motivated by previous research, this research will use DenseNet architecture (Dense Convolutional Network) to detect diseases in rice plants. The dataset used is relatively small, between 100-200 photos for each disease. To cover the lack of dataset augmentation is done to the dataset. The final results obtained are quite satisfactory with an accuracy of 96% with a Weighted Average of 97%.