Corn is the second most critical crop in Myanmar’s agricultural sector in livestock feed production and export income after rice. On the other sides, corn plant suffers various leaf diseases under different weather conditions, this significantly leads to the loss in the economy of Myanmar yearly. Farmers mostly lack the knowledge of disease symptoms to give the right treatment and experts are also rare. In recent year, in agricultural sector, with the advancement of deep learning technologies, researchers used to apply these techniques for the classification of leaf diseases. This paper presents an automatic leaf disease classification system on the local Myanmar corn dataset using a lightweight ResNet-9 model. The study also compares two training methods: direct fine-tuning and transfer learning. The direct fine-tuning approach achieved a test accuracy of 97.97%, while the transfer learning approach reached a higher test accuracy of 99.63%. The transfer learning model also showed more stable convergence by using pre-trained weights learned from global datasets. Experimental results on a local corn leaf dataset demonstrate that the proposed ResNet-9 model can provide accurate disease classification with low computational cost, making it suitable for deployment in resource-limited agricultural environments.
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Corn leaf disease, ResNet-9, Transfer Learning, Myanmar Agriculture, Deep Learning, Image Classification.
