Corn Disease Classification Using Transfer Learning and Convolutional Neural Network

Faisal Dharma Adhinata, Gita Fadila Fitriana, Aditya Wijayanto, Muhammad Pajar Kharisma Putra


Indonesia is an agricultural country with abundant agricultural products. One of the crops used as a staple food for Indonesians is corn. This corn plant must be protected from diseases so that the quality of corn harvest can be optimal. Early detection of disease in corn plants is needed so that farmers can provide treatment quickly and precisely. Previous research used machine learning techniques to solve this problem. The results of the previous research were not optimal because the amount of data used was slightly and less varied. Therefore, we propose a technique that can process lots and varied data, hoping that the resulting system is more accurate than the previous research. This research uses transfer learning techniques as feature extraction combined with Convolutional Neural Network as a classification. We analysed the combination of DenseNet201 with a Flatten or Global Average Pooling layer. The experimental results show that the accuracy produced by the combination of DenseNet201 with the Global Average Pooling layer is better than DenseNet201 with Flatten layer. The accuracy obtained is 93% which proves the proposed system is more accurate than previous studies.


Convolutional Neural Network, corn plant, DenseNet201, flatten layer, global average pooling layer


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DOI: 10.30595/juita.v9i2.11686


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ISSN: 2579-8901