Identification of Corn Leaf Disease Using Deep Learning

The agricultural production can be enhanced effectively by detecting corp diseases more efficiently thus, preventing crop failure due to contamination. This study utilized deep convolutional neural network (CNN) models to detect plant leaf diseases from leaf images. CNNs have demonstrated promising results for machine vision tasks. The models achieved accuracy rates ranging from 90.25% to 95.56% to classify the disease, surpassing traditional methods that rely on manually crafted features. Key CNN architectures explored include VGG16, ResNet50, and AlexNet, with optimizations such as batch size ad-justments, dropout incorporation, and varying training epochs. The high accuracy levels indicate that deep CNNs can reliably detect diseases, suggesting their potential for enabling automated crop disease detection in real-time agricultural systems. Moving forward, additional testing with expanded datasets of plant diseases could further validate the robustness of the models. Deep learning methods can be used to precisely predict the crop diseases, this approach may significantly aid efforts in early disease intervention and treatment, leading to improved crop health, yields, and ultimately, farm productivity.