A Fine-Tuned DenseNet Model for an Efficient Maize Leaf Disease Classification

The detection of maize leaf disease is essential for food security and sustainable agriculture. Early maize leaf disease detection enables timely treatment, limiting crop losses and stopping the replication of diseases. Precise identification facilitates the application of focused interventions, maximizes the use of available resources, and guarantees a more robust yield of maize. In the end, this helps to sustain a steady supply of food and the livelihoods of those who depend on maize farming. This work investigates the use of a DenseNet model for maize leaf disease detection. The model has been supplied with different learning rate values as 0.1, 0.01, 0.001, and 0.0001. The optimizers during the implementation have been also changed to Adam and SGD. The highest accuracy has been achieved by the Adam optimizer at a learning rate of 0.01. The model performs best, demonstrating a 96.53% training accuracy and an even higher 97.43% testing accuracy. The loss of the model has been also analyzed as 0.1433, and 0.0948, during the training and testing phases. The results highlight denseNet’s effectiveness in precisely identifying diseases of the maize leaf, with promising implications for agricultural diagnostics. In the future, the model’s performance can be increased by expanding its identification of a wider variety of maize leaf diseases and combining the model with innovative agricultural technologies to monitor diseases in real-time and react quickly to new challenges associated with leaf diseases.