Comparative Analysis of Maize Leaf Disease Detection using Convolutional Neural Networks

Worldwide, maize is a significant cereal crop for crop productivity, identifying diseases in the plant's leaves is essential to raise a good crop. Deep learning methods that have been used in recent years to precisely identify and categorize these serious diseases, offering a non-destructive and effective way to find maize leaf ailments. In order to detect maize leaf disease, this paper suggests using three well-liked deep learning models: VGG16, Inception V3, and EfficientNet. The models were trained and assessed using a datasets of 4000 images of three distinct maize leaf diseases and a healthy class. All three models had high accuracy rates, according to the results, though EfficientNet outperformed the other two models. The suggested method can detect and track diseases in maize crops with high accuracy and can be applied practically. It can accurately classify various diseases. The study also demonstrates that deep learning models can offer a trustworthy and effective solution for detecting crop diseases, which can aid in lowering crop losses, raising crop yields, and enhancing food security.